diff --git a/.env.example b/.env.example new file mode 100644 index 0000000..a87280d --- /dev/null +++ b/.env.example @@ -0,0 +1,2 @@ +# Google Gemini API Key (required) +GOOGLE_API_KEY=your_google_api_key_here diff --git a/.github/workflows/docker-build.yml b/.github/workflows/docker-build.yml new file mode 100644 index 0000000..f012556 --- /dev/null +++ b/.github/workflows/docker-build.yml @@ -0,0 +1,101 @@ +name: Build Docker Images + +on: + push: + branches: [main, langchain] + pull_request: + branches: [main] + +env: + REGISTRY: ghcr.io + BACKEND_IMAGE: ${{ github.repository }}/backend + FRONTEND_IMAGE: ${{ github.repository }}/frontend + +jobs: + build-backend: + name: Build Backend + runs-on: ubuntu-latest + permissions: + contents: read + packages: write + + steps: + - name: Checkout repository + uses: actions/checkout@v4 + + - name: Set up Docker Buildx + uses: docker/setup-buildx-action@v3 + + - name: Log in to Container Registry + if: github.event_name != 'pull_request' + uses: docker/login-action@v3 + with: + registry: ${{ env.REGISTRY }} + username: ${{ github.actor }} + password: ${{ secrets.GITHUB_TOKEN }} + + - name: Extract metadata for Backend + id: meta + uses: docker/metadata-action@v5 + with: + images: ${{ env.REGISTRY }}/${{ env.BACKEND_IMAGE }} + tags: | + type=ref,event=branch + type=ref,event=pr + type=sha,prefix= + type=raw,value=latest + + - name: Build and push Backend image + uses: docker/build-push-action@v5 + with: + context: ./apps/backend + file: ./apps/backend/Dockerfile + push: ${{ github.event_name != 'pull_request' }} + tags: ${{ steps.meta.outputs.tags }} + labels: ${{ steps.meta.outputs.labels }} + cache-from: type=gha + cache-to: type=gha,mode=max + + build-frontend: + name: Build Frontend + runs-on: ubuntu-latest + permissions: + contents: read + packages: write + + steps: + - name: Checkout repository + uses: actions/checkout@v4 + + - name: Set up Docker Buildx + uses: docker/setup-buildx-action@v3 + + - name: Log in to Container Registry + if: github.event_name != 'pull_request' + uses: docker/login-action@v3 + with: + registry: ${{ env.REGISTRY }} + username: ${{ github.actor }} + password: ${{ secrets.GITHUB_TOKEN }} + + - name: Extract metadata for Frontend + id: meta + uses: docker/metadata-action@v5 + with: + images: ${{ env.REGISTRY }}/${{ env.FRONTEND_IMAGE }} + tags: | + type=ref,event=branch + type=ref,event=pr + type=sha,prefix= + type=raw,value=latest + + - name: Build and push Frontend image + uses: docker/build-push-action@v5 + with: + context: ./apps/frontend + file: ./apps/frontend/Dockerfile + push: ${{ github.event_name != 'pull_request' }} + tags: ${{ steps.meta.outputs.tags }} + labels: ${{ steps.meta.outputs.labels }} + cache-from: type=gha + cache-to: type=gha,mode=max diff --git a/README.md b/README.md old mode 100644 new mode 100755 index 0124771..b3ba81e --- a/README.md +++ b/README.md @@ -1,90 +1,83 @@ -# Sentiment Analysis Project +# Scholar Sense -## Project Overview -This project provides a comprehensive sentiment analysis solution with a modern web interface for analyzing PDF documents. The system can determine sentiment towards specific topics in uploaded documents and extract relevant keywords and sentences. +A full-stack Retrieval-Augmented Generation (RAG) application for sentiment analysis of academic research papers. - +## Features -## Read our wiki -- [Design](https://git.ecdf.ed.ac.uk/psd2425/Rose-Campbell/sentiment-analysis/-/wikis/Design) -- [Planning](https://git.ecdf.ed.ac.uk/psd2425/Rose-Campbell/sentiment-analysis/-/wikis/Planning) -- [Implementation](https://git.ecdf.ed.ac.uk/psd2425/Rose-Campbell/sentiment-analysis/-/wikis/Implementation) +- **PDF Upload**: Upload academic research papers in PDF format +- **Sentiment Analysis**: Analyze paper sentiment towards specific keywords +- **Citation Support**: Extract and display citations with page references +- **Interactive Chat**: Query-based interface for analysis +- **Modern UI**: Next.js with Tailwind CSS -## System Architecture -The project is built as a monorepo using Turborepo to manage multiple services: +## Tech Stack -1. **Frontend**: Next.js application that provides the user interface -2. **Backend**: Flask-based API service that performs the sentiment analysis +### Backend +- **FastAPI**: High-performance Python web framework +- **LangChain**: RAG pipeline orchestration +- **ChromaDB**: Vector database for embeddings +- **Google Gemini**: LLM for analysis +- **PyMuPDF & pdfplumber**: PDF processing -### Architecture Diagram -``` -┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ -│ │ │ │ │ │ -│ Frontend │ ──────► │ Backend │ ──────► │ GROBID Service │ -│ (Next.js) │ │ (Flask) │ │ (PDF Parser) │ -│ │ │ │ │ │ -└─────────────────┘ └─────────────────┘ └─────────────────┘ - -``` - -## Key Features -- Upload and analyze PDF documents -- Perform topic-focused sentiment analysis -- Extract relevant sentences and keywords -- Visual representation of sentiment results - -## Getting Started - -### Prerequisites -- Node.js 18.0+ -- Python 3.8+ -- Docker (for GROBID service) +### Frontend +- **Next.js 14**: React framework with App Router +- **Tailwind CSS**: Utility-first styling +- **TypeScript**: Type-safe development +- **Axios**: HTTP client -### Setup Steps +## Project Structure -1. **Clone the repository** -```bash -git clone https://git.ecdf.ed.ac.uk/psd2425/Rose-Campbell/sentiment-analysis.git -cd sentiment-analysis ``` - -2. **Start the GROBID service** (required for PDF processing) -```bash -cd apps/backend/grobid_deployment && chmod +x deploy-grobid.sh -./deploy-grobid.sh +windows-build/ +├── backend/ +│ ├── main.py # FastAPI application +│ ├── config.py # Configuration settings +│ ├── requirements.txt # Python dependencies +│ ├── .env # Environment variables +│ ├── routers/ +│ │ ├── upload.py # PDF upload endpoints +│ │ └── chat.py # Chat/analysis endpoints +│ ├── services/ +│ │ ├── pdf_processor.py # PDF extraction and chunking +│ │ └── rag_service.py # RAG pipeline and LLM +│ ├── uploads/ # Uploaded PDFs (auto-created) +│ └── chroma_db/ # Vector database (auto-created) +│ +└── frontend/ + ├── app/ + │ ├── layout.tsx # Root layout + │ ├── page.tsx # Main page with tabs + │ └── globals.css # Global styles + ├── components/ + │ ├── UploadTab.tsx # Upload interface + │ └── ChatTab.tsx # Chat interface + ├── package.json + ├── tsconfig.json + ├── tailwind.config.js + └── next.config.js ``` -3. **Set up the backend** -```bash -cd apps/backend && chmod +x start_conda.sh -./start_conda.sh -``` +## Setup Instructions -4. **Set up the frontend** -```bash -cd apps/frontend -npm install -npm run dev -``` +### Prerequisites +- Python 3.9+ +- Node.js 18+ +- Google API Key (for Gemini) -5. **Access the application** -- Frontend: http://localhost:3000 -- Backend API: http://localhost:5000 -- GROBID Service: http://localhost:8070 +### Backend Setup -## Directory Structure -``` -sentiment-analysis/ -├── apps/ -│ ├── frontend/ # Next.js frontend application -│ └── backend/ # Flask backend service -├── packages/ # Shared packages and utilities -├── README.md # This file -└── package.json # Root package.json for Turborepo +1. Navigate to backend directory: +```powershell +cd backend ``` -## Detailed Documentation -For more detailed information about each component: +2. Create a virtual environment: +```powershell +python -m venv venv +.\venv\Scripts\Activate +``` -- [Frontend Documentation](apps/frontend/README.md) -- [Backend Documentation](apps/backend/README.md) \ No newline at end of file +3. Install dependencies: +```powershell +pip install -r requirements.txt +``` \ No newline at end of file diff --git a/apps/backend/.dockerignore b/apps/backend/.dockerignore new file mode 100644 index 0000000..d0a36be --- /dev/null +++ b/apps/backend/.dockerignore @@ -0,0 +1,18 @@ +venv/ +__pycache__/ +*.pyc +*.pyo +*.pyd +.Python +.env +.git +.gitignore +*.md +.pytest_cache/ +.coverage +htmlcov/ +.mypy_cache/ +uploads/* +chroma_db/* +!uploads/.gitkeep +!chroma_db/.gitkeep diff --git a/apps/backend/.gitignore b/apps/backend/.gitignore old mode 100644 new mode 100755 index 4ccfbfa..eede5b7 --- a/apps/backend/.gitignore +++ b/apps/backend/.gitignore @@ -1,5 +1,6 @@ -.env.example -/uploads -/testing -/__pycache__ -/logs \ No newline at end of file +uploads/ +chroma_db/ +__pycache__/ +*.pyc +venv/ +.env diff --git a/apps/backend/API_DOCS.md b/apps/backend/API_DOCS.md deleted file mode 100644 index 624b14b..0000000 --- a/apps/backend/API_DOCS.md +++ /dev/null @@ -1,536 +0,0 @@ -# ScholarSense API Documentation - -## Overview -ScholarSense provides a RESTful API for academic paper analysis using Retrieval-Augmented Generation (RAG). Upload PDF papers, query them with natural language questions, and receive citation-grounded answers. - -## Base URL -``` -http://localhost:5000 -``` - -## Authentication -Currently no authentication required (add for production). - ---- - -## Endpoints - -### 1. System Status - -#### GET `/status` -Check system health and RAG statistics. - -**Response:** -```json -{ - "status": "ok", - "timestamp": "2025-11-14T10:30:00", - "services": { - "grobid": { - "available": true, - "url": "http://localhost:8070" - } - }, - "rag_stats": { - "total_papers": 5, - "total_chunks": 450, - "embedding_model": "BAAI/bge-large-en-v1.5", - "embedding_dimension": 1024, - "llm_model": "gemini-pro" - } -} -``` - ---- - -### 2. Upload Paper - -#### POST `/upload` -Upload and ingest an academic PDF paper into the RAG system. - -**Request:** -- Content-Type: `multipart/form-data` -- Body: `file` (PDF file) - -**Response:** -```json -{ - "status": "ok", - "result": { - "paper_id": "abc123def456...", - "title": "Attention Is All You Need", - "chunks_processed": 45, - "sections_processed": 7, - "filename": "transformer_paper.pdf", - "upload_date": "2025-11-14T10:35:00" - } -} -``` - ---- - -### 3. Query Papers - -#### POST `/query` -Query uploaded papers using natural language. - -**Request:** -```json -{ - "query": "What are the main advantages of transformers over RNNs?", - "paper_ids": ["abc123", "def456"], // Optional: specific papers - "n_results": 5 // Optional: number of context chunks (default: 5) -} -``` - -**Response:** -```json -{ - "status": "ok", - "result": { - "answer": "Transformers offer several advantages over RNNs [1][2]...", - "citations": [ - { - "citation_number": 1, - "paper_title": "Attention Is All You Need", - "section": "Introduction", - "page": "2", - "paper_id": "abc123" - } - ], - "contexts": [ - { - "text": "Unlike RNNs, transformers can process...", - "metadata": { - "paper_id": "abc123", - "title": "Attention Is All You Need", - "section": "Introduction", - "page": "2" - }, - "relevance_score": 0.89 - } - ], - "model": "gemini-pro", - "contexts_used": 5 - } -} -``` - ---- - -### 4. List Papers - -#### GET `/papers` -List all uploaded papers. - -**Response:** -```json -{ - "status": "ok", - "papers": [ - { - "paper_id": "abc123", - "title": "Attention Is All You Need", - "authors": "Vaswani et al.", - "year": "2017", - "upload_date": "2025-11-14T10:35:00" - } - ], - "total": 1 -} -``` - ---- - -### 5. Delete Paper - -#### DELETE `/papers/` -Delete a paper from the system. - -**Response:** -```json -{ - "status": "ok", - "message": "Paper abc123 deleted successfully" -} -``` - ---- - -### 6. Compare Papers - -#### POST `/compare` -Compare multiple papers across different aspects. - -**Request:** -```json -{ - "paper_ids": ["abc123", "def456", "ghi789"], - "aspects": ["methodology", "results", "conclusions", "limitations"] -} -``` - -**Response:** -```json -{ - "status": "ok", - "result": { - "comparison": "Paper 1 and Paper 2 both use transformer architectures...", - "papers_compared": 3, - "aspects": ["methodology", "results", "conclusions", "limitations"], - "model": "gemini-pro", - "papers": [ - { - "paper_id": "abc123", - "title": "Attention Is All You Need", - "abstract": "..." - } - ] - } -} -``` - ---- - -## Error Responses - -All endpoints return errors in this format: - -```json -{ - "status": "error", - "error": "Error message description" -} -``` - -**Common HTTP Status Codes:** -- `200 OK` - Success -- `400 Bad Request` - Invalid input -- `404 Not Found` - Resource not found -- `413 Payload Too Large` - File too large -- `500 Internal Server Error` - Server error - ---- - -## Example Usage - -### Python - -```python -import requests - -# Upload a paper -with open('paper.pdf', 'rb') as f: - response = requests.post( - 'http://localhost:5000/upload', - files={'file': f} - ) - paper_data = response.json() - paper_id = paper_data['result']['paper_id'] - -# Query the paper -response = requests.post( - 'http://localhost:5000/query', - json={ - 'query': 'What is the main contribution of this paper?', - 'paper_ids': [paper_id] - } -) -answer = response.json()['result']['answer'] -print(answer) -``` - -### cURL - -```bash -# Upload paper -curl -X POST http://localhost:5000/upload \ - -F "file=@paper.pdf" - -# Query papers -curl -X POST http://localhost:5000/query \ - -H "Content-Type: application/json" \ - -d '{"query": "What are the key findings?"}' - -# List papers -curl http://localhost:5000/papers -``` - ---- - -## Old Sentiment Analysis API (Deprecated) - -This document provides detailed information about the sentiment analysis backend API for frontend developers. - -## Basic Information - -- Base URL: `http://localhost:5000` -- Content-Type for all POST requests should be: `application/json` -- All responses are in JSON format - -## API Endpoints - -### 1. Analyze Text or File - -**Endpoint:** `/analyse` - -**Method:** POST - -**Description:** Analyze the sentiment of text content or uploaded files - -**Request Body:** - -Analyze text: -```json -{ - "text": "Text content to analyze", - "title": "Optional text title", - "method": "vader|textblob|combined", - "full_analysis": false -} -``` - -Analyze file: -```json -{ - "file_path": "path/to/uploaded/file" -} -``` - -Analyze topic-based content: -```json -{ - "topic": "Topic to analyze", - "file_path": "path/to/uploaded/file" -} -``` - -**Success Response:** -```json -{ - "status": "ok", - "result": { - "sentiment": "positive|negative|neutral", - "score": 0.75, - "confidence": 0.85, - "details": { - "pos": 0.8, - "neg": 0.1, - "neu": 0.1, - "compound": 0.75 - } - } -} -``` - -**Error Response:** -```json -{ - "status": "error", - "error": "Error message" -} -``` - -### 2. Upload File - -**Endpoint:** `/upload` - -**Method:** POST - -**Description:** Upload a file for subsequent analysis - -**Request Body:** -- Use `multipart/form-data` format -- File field name should be `file` -- Supported file types: PDF, TXT, JSON - -**Success Response:** -```json -{ - "status": "ok", - "message": "File uploaded successfully", - "file_path": "path/to/uploaded/file", - "file_name": "Original file name", - "file_type": "File type" -} -``` - -**Error Response:** -```json -{ - "status": "error", - "error": "Error message" -} -``` - -### 3. Access Uploaded File - -**Endpoint:** `/files/` - -**Method:** GET - -**Description:** Get an uploaded file - -**Parameters:** -- `filename`: Name of the uploaded file - -**Response:** File content - -### 4. Check Service Status - -**Endpoint:** `/status` - -**Method:** GET - -**Description:** Check backend service status and dependencies - -**Success Response:** -```json -{ - "status": "ok", - "timestamp": "2024-03-21T10:00:00", - "services": { - "grobid": { - "available": true, - "url": "http://localhost:8070" - } - } -} -``` - -**Error Response:** -```json -{ - "status": "error", - "error": "Error message", - "timestamp": "2024-03-21T10:00:00" -} -``` - -### 5. Analyze Topic - -**Endpoint:** `/analyze_topic` - -**Method:** POST - -**Description:** Analyze the sentiment of topic-related text - -**Request Body:** -```json -{ - "topic": "Topic text to analyze" -} -``` - -**Success Response:** -```json -{ - "status": "ok", - "result": { - "topic": "Topic text", - "sentiment": "positive|negative|neutral", - "score": 0.75, - "confidence": 0.85, - "keywords": [ - { - "word": "keyword1", - "count": 5 - } - ], - "sample_text": "Sample text related to the topic..." - } -} -``` - -**Error Response:** -```json -{ - "status": "error", - "error": "Error message" -} -``` - -## Usage Examples - -### Example 1: Analyze Text - -```javascript -// Frontend code example -async function analyzeText(text) { - const response = await fetch('http://localhost:5000/analyse', { - method: 'POST', - headers: { - 'Content-Type': 'application/json', - }, - body: JSON.stringify({ - text, - method: 'combined', - full_analysis: false - }), - }); - - const data = await response.json(); - if (data.status === 'ok') { - return data.result; - } else { - throw new Error(data.error); - } -} -``` - -### Example 2: Upload and Analyze File - -```javascript -// Frontend code example -async function uploadAndAnalyzeFile(file) { - // Upload file - const formData = new FormData(); - formData.append('file', file); - - const uploadResponse = await fetch('http://localhost:5000/upload', { - method: 'POST', - body: formData, - }); - - const uploadData = await uploadResponse.json(); - if (uploadData.status !== 'ok') { - throw new Error(uploadData.error); - } - - // Analyze uploaded file - const analyzeResponse = await fetch('http://localhost:5000/analyse', { - method: 'POST', - headers: { - 'Content-Type': 'application/json', - }, - body: JSON.stringify({ - file_path: uploadData.file_path, - full_analysis: true - }), - }); - - const analyzeData = await analyzeResponse.json(); - if (analyzeData.status === 'ok') { - return analyzeData.result; - } else { - throw new Error(analyzeData.error); - } -} -``` - -## Error Codes -- 400: Bad Request - Invalid input data -- 404: Not Found - Resource not found -- 413: Payload Too Large - File size exceeds limit -- 500: Internal Server Error - Server-side error - -## Rate Limiting -- Maximum file size: 16MB -- Supported file types: PDF, TXT, JSON -- Concurrent requests: Limited by server configuration - -## Security Considerations -1. All file uploads are validated for type and size -2. File paths are sanitized to prevent directory traversal -3. CORS is enabled for frontend access -4. File content is validated before processing \ No newline at end of file diff --git a/apps/backend/Dockerfile b/apps/backend/Dockerfile index 1790bb8..7153bd5 100644 --- a/apps/backend/Dockerfile +++ b/apps/backend/Dockerfile @@ -1,49 +1,22 @@ -FROM ubuntu:22.04 - -# Prevent interactive prompts during package installation -ENV DEBIAN_FRONTEND=noninteractive +FROM python:3.12-slim WORKDIR /app -# Install Python 3.10 and build essentials -RUN apt-get update && apt-get install -y \ - python3.10 \ - python3-pip \ - python3.10-dev \ - build-essential \ - curl \ - && rm -rf /var/lib/apt/lists/* - -# Create symlink for python command -RUN ln -s /usr/bin/python3.10 /usr/bin/python - -# First install numpy and scipy with compatible versions -RUN pip install --no-cache-dir numpy==1.24.2 scipy==1.10.1 - -# Install Python dependencies +# Copy requirements first for better caching COPY requirements.txt . -RUN pip install --no-cache-dir -r requirements.txt - -# Download NLTK data -RUN python -c "import nltk; nltk.download('punkt'); nltk.download('stopwords'); nltk.download('wordnet'); nltk.download('averaged_perceptron_tagger')" -# Pre-download embedding model to avoid runtime download -RUN python -c "from sentence_transformers import SentenceTransformer; SentenceTransformer('BAAI/bge-large-en-v1.5')" +# Install Python dependencies +RUN pip install --no-cache-dir --upgrade pip && \ + pip install --no-cache-dir -r requirements.txt # Copy application code COPY . . -# Create upload directory -RUN mkdir -p uploads logs - -# Set environment variables -ENV PYTHONUNBUFFERED=1 -ENV PORT=5000 -# Default GROBID URL, can be overridden at runtime -ENV GROBID_URL=http://grobid-service:8070 +# Create directories for data persistence +RUN mkdir -p /app/uploads /app/chroma_db # Expose port -EXPOSE 5000 +EXPOSE 8000 -# Start application -CMD ["python", "app.py"] \ No newline at end of file +# Run the application +CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"] diff --git a/apps/backend/Dockerfile.dev b/apps/backend/Dockerfile.dev new file mode 100644 index 0000000..83cce4a --- /dev/null +++ b/apps/backend/Dockerfile.dev @@ -0,0 +1,19 @@ +FROM python:3.12-slim + +WORKDIR /app + +# Copy requirements first for better caching +COPY requirements.txt . + +# Install Python dependencies +RUN pip install --no-cache-dir --upgrade pip && \ + pip install --no-cache-dir -r requirements.txt + +# Create directories for data persistence +RUN mkdir -p /app/uploads /app/chroma_db + +# Expose port +EXPOSE 8000 + +# Run with hot-reload +CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000", "--reload"] diff --git a/apps/backend/README.md b/apps/backend/README.md deleted file mode 100644 index c2b7d2e..0000000 --- a/apps/backend/README.md +++ /dev/null @@ -1,131 +0,0 @@ -# Sentiment Analysis Backend Service - -## Project Overview -A Flask-based sentiment analysis backend service that provides text and document sentiment analysis capabilities. The service supports multiple file formats (PDF, TXT, JSON) and can perform topic-based text analysis. - -## Technology Stack -- **Python 3.8+**: Main programming language -- **Flask**: Web framework -- **NLTK**: Natural Language Processing toolkit -- **TextBlob**: Text analysis library -- **GROBID**: PDF document parsing service -- **scikit-learn**: Machine learning library for text feature extraction -- **Flask-CORS**: Cross-origin resource sharing handler - -## Project Structure -``` -backend/ -├── API_DOCS.md # API documentation -├── grobid_deployment # GROBID deployment -│ ├─ deploy-grobid.sh -│ ├─ grobid-api-example.py -│ └─ grobid-docker-deployment.md -├── app.py # Main application entry point -├── document_processor.py # Document processing module -├── sentiment_analyzer.py # Sentiment analysis module -├── text_processor.py # Text processing module -├── logging_config.py # Logging configuration module -├── requirements.txt # Project dependencies -├── start_conda.sh # Startup script -├── uploads/ # Uploaded files directory -└── logs/ # Log files directory -``` - -## Environment Requirements -- Python 3.8 or higher -- [Conda](https://docs.conda.io/en/latest/) environment manager -- [GROBID](https://grobid.readthedocs.io/en/latest/) service (for PDF processing) - -## Deployment Steps - -1. Deploy GROBID service: -The full instructions are available in the [grobid_deployment/grobid-docker-deployment.md](grobid_deployment/grobid-docker-deployment.md) file. -```bash -cd grobid_deployment -./deploy-grobid.sh -``` -Check GROBID service status: -```bash -curl http://localhost:8070/api/isalive -``` - -2. Start the sentiment analysis service: -```bash -./start_conda.sh -``` - -## Configuration -- `UPLOAD_FOLDER`: Directory for uploaded files (default: 'uploads') -- `MAX_CONTENT_LENGTH`: Maximum file size limit (default: 16MB) -- `ALLOWED_EXTENSIONS`: Allowed file types (pdf, txt, text, json) -- `LOG_DIR`: Directory for log files (default: 'logs') - - -## API Endpoints - -### 1. Status Check -- **URL**: `/status` -- **Method**: GET -- **Description**: Check service status and dependency availability - -### 2. Text Analysis -- **URL**: `/analyse` -- **Method**: POST -- **Features**: - - Text sentiment analysis - - File processing - - Topic-based analysis - -### 3. File Upload -- **URL**: `/upload` -- **Method**: POST -- **Feature**: Upload and analyze files - -### 4. File Retrieval -- **URL**: `/files/` -- **Method**: GET -- **Feature**: Retrieve uploaded files - -### 5. Topic Analysis -- **URL**: `/analyze_topic` -- **Method**: POST -- **Feature**: Analyze text topics and keywords - -## Logging System -- Log file location: `logs/app.log` -- Log level: INFO -- Log format: `%(asctime)s - %(name)s - %(levelname)s - %(message)s` - -## Error Handling -The service includes the following error handling: -- 413: File size limit exceeded -- 404: Resource not found -- 500: Internal server error - -## Development Notes -1. Ensure `DEBUG=True` is set in development environment -2. Run service directly using `python app.py` -3. Logs are output to both console and file - -## Important Notes -1. Ensure GROBID service is running properly -2. Regularly clean temporary files in the `uploads` directory -3. Monitor log file size and rotate when necessary -4. Set appropriate file size limits in production environment - -## Troubleshooting -1. If service fails to start, check: - - Conda environment is properly activated - - All dependencies are correctly installed - - GROBID service is running - - Port is not in use - -2. If file upload fails, check: - - File size is within limits - - File type is allowed - - Upload directory permissions are correct - -3. If analysis fails, check: - - Error messages in log file - - Input text format is correct - - GROBID service is responding properly \ No newline at end of file diff --git a/apps/backend/README_NEW.md b/apps/backend/README_NEW.md deleted file mode 100644 index 2399abe..0000000 --- a/apps/backend/README_NEW.md +++ /dev/null @@ -1,195 +0,0 @@ -# ScholarSense Backend - -## Overview -RAG-powered backend for academic paper analysis using Flask, ChromaDB, and Google Gemini. - -## Features -- **PDF Processing**: Extract structured content from academic PDFs using GROBID -- **Vector Search**: Store and retrieve document embeddings with ChromaDB -- **RAG Pipeline**: Question answering with citation grounding -- **Multi-Paper Comparison**: Compare methodologies, results, and conclusions -- **RESTful API**: Clean API for frontend integration - -## Architecture -``` -┌──────────────┐ -│ Flask API │ -└──────┬───────┘ - │ - ┌───┴────┬────────┬──────────┐ - ▼ ▼ ▼ ▼ -GROBID ChromaDB Gemini Embeddings -(PDF) (Vector) (LLM) (BGE) -``` - -## Prerequisites -- Python 3.10+ -- GROBID service running on port 8070 -- Google Gemini API key - -## Installation - -### 1. Install Dependencies -```bash -cd apps/backend -pip install -r requirements.txt -``` - -### 2. Download NLTK Data (first time only) -```python -python -c "import nltk; nltk.download('punkt'); nltk.download('stopwords')" -``` - -### 3. Configure Environment -```bash -cp .env.example .env -# Edit .env and add your GEMINI_API_KEY -``` - -### 4. Start GROBID -```bash -# Using Docker -docker pull lfoppiano/grobid:0.8.0 -docker run -d -p 8070:8070 lfoppiano/grobid:0.8.0 -``` - -## Running the Backend - -### Development -```bash -export GEMINI_API_KEY="your_key_here" -python app.py -``` - -### Production -```bash -gunicorn -w 4 -b 0.0.0.0:5000 app:app -``` - -## API Documentation -See [API_DOCS.md](./API_DOCS.md) for complete API reference. - -## Quick Start - -```bash -# 1. Upload a paper -curl -X POST http://localhost:5000/upload \ - -F "file=@paper.pdf" - -# 2. Query the paper -curl -X POST http://localhost:5000/query \ - -H "Content-Type: application/json" \ - -d '{"query": "What is the main contribution?"}' - -# 3. List papers -curl http://localhost:5000/papers -``` - -## Core Modules - -### `rag_engine.py` -Main RAG pipeline orchestrator. Handles: -- Document chunking -- Embedding generation -- Vector storage -- Query processing -- Answer generation - -### `vector_store.py` -ChromaDB wrapper for vector operations: -- Add documents with embeddings -- Semantic search -- Paper management - -### `embeddings.py` -Embedding generation using BGE-large-en-v1.5: -- Text encoding -- Query encoding with instruction prefix -- Batch processing - -### `llm_client.py` -Google Gemini API integration: -- Citation-grounded Q&A -- Multi-paper comparison -- Stance detection - -### `document_processor.py` -PDF processing with GROBID: -- Structured extraction (title, authors, sections) -- Section identification -- Metadata extraction - -### `grobid_client.py` -GROBID API client: -- Full-text PDF processing -- TEI XML parsing -- Section extraction - -## Environment Variables - -| Variable | Default | Description | -|----------|---------|-------------| -| `GEMINI_API_KEY` | - | **Required** Google Gemini API key | -| `GROBID_URL` | `http://localhost:8070` | GROBID service URL | -| `UPLOAD_FOLDER` | `uploads` | Upload directory | -| `VECTOR_STORE_PATH` | `./chroma_db` | ChromaDB storage path | -| `EMBEDDING_MODEL` | `BAAI/bge-large-en-v1.5` | Embedding model | -| `PORT` | `5000` | Flask port | -| `DEBUG` | `False` | Debug mode | - -## Testing - -```bash -# Run tests -python -m pytest test/ - -# Test specific endpoint -python test/test_upload_file.py -``` - -## Troubleshooting - -### GROBID Not Available -```bash -# Check GROBID status -curl http://localhost:8070/api/version - -# Restart GROBID if needed -docker restart -``` - -### ChromaDB Issues -```bash -# Clear vector database -rm -rf ./chroma_db -# Backend will recreate on next start -``` - -### Gemini API Errors -- Verify API key in `.env` -- Check API quota/limits -- Ensure billing is enabled - -## Directory Structure -``` -apps/backend/ -├── app.py # Main Flask application -├── rag_engine.py # RAG pipeline -├── vector_store.py # ChromaDB wrapper -├── embeddings.py # Embedding generation -├── llm_client.py # Gemini API client -├── document_processor.py # PDF processing -├── grobid_client.py # GROBID API client -├── requirements.txt # Python dependencies -├── .env.example # Example environment config -├── uploads/ # Uploaded PDFs -├── chroma_db/ # Vector database -└── logs/ # Application logs -``` - -## Development Tips - -1. **First Run**: The embedding model (~1.3GB) will download on first use -2. **Memory**: Recommend 8GB+ RAM for embedding model and vector store -3. **GROBID**: Must be running before uploading papers -4. **API Key**: Get Gemini API key from https://makersuite.google.com/app/apikey diff --git a/apps/backend/app.py b/apps/backend/app.py deleted file mode 100644 index c262d70..0000000 --- a/apps/backend/app.py +++ /dev/null @@ -1,362 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- - -""" -ScholarSense Backend API -Flask application providing RAG-powered academic paper Q&A -""" - -import os -import json -import logging -from pathlib import Path -from datetime import datetime -from flask import Flask, request, jsonify, send_from_directory -from flask_cors import CORS -from werkzeug.utils import secure_filename - -from document_processor import DocumentProcessor -from rag_engine import RAGEngine -from logging_config import setup_logging -import sys - -# Setup logging first -logger = setup_logging() - -# Get the absolute path of the backend directory -BACKEND_DIR = os.path.dirname(os.path.abspath(__file__)) -LOG_DIR = os.path.join(BACKEND_DIR, 'logs') - -try: - # Create logs directory with full permissions - os.makedirs(LOG_DIR, mode=0o777, exist_ok=True) - print(f"Created/verified log directory at: {LOG_DIR}") -except Exception as e: - print(f"Error creating log directory: {e}", file=sys.stderr) - # Fallback to /tmp if we can't create in the app directory - LOG_DIR = '/tmp/sentiment_logs' - os.makedirs(LOG_DIR, mode=0o777, exist_ok=True) - print(f"Using fallback log directory at: {LOG_DIR}") - -LOG_FILE = os.path.join(LOG_DIR, 'app.log') - -# Initialize Flask app -app = Flask(__name__) -CORS(app) # Enable CORS for all routes - -# Configure upload settings -UPLOAD_FOLDER = os.environ.get('UPLOAD_FOLDER', 'uploads') -ALLOWED_EXTENSIONS = {'pdf'} # Only PDF for academic papers -MAX_CONTENT_LENGTH = 50 * 1024 * 1024 # 50MB for academic papers - -# Ensure upload directory exists -os.makedirs(UPLOAD_FOLDER, exist_ok=True) - -# Configure app -app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER -app.config['MAX_CONTENT_LENGTH'] = MAX_CONTENT_LENGTH - -# Initialize processors -document_processor = DocumentProcessor() - -# Initialize RAG engine (lazy loaded) -_rag_engine = None - -def get_rag_engine(): - """Get or create RAG engine instance""" - global _rag_engine - if _rag_engine is None: - try: - logger.info("Initializing RAG engine...") - _rag_engine = RAGEngine( - vector_store_path=os.environ.get('VECTOR_STORE_PATH', './chroma_db'), - embedding_model=os.environ.get('EMBEDDING_MODEL', 'BAAI/bge-large-en-v1.5'), - llm_api_key=os.environ.get('GEMINI_API_KEY') - ) - logger.info("RAG engine initialized successfully") - except Exception as e: - logger.error(f"Error initializing RAG engine: {e}") - raise - return _rag_engine - -def allowed_file(filename): - """Check if file has an allowed extension""" - return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS - -@app.route('/status', methods=['GET']) -def status(): - """API endpoint to check service status""" - try: - # Check services - services = document_processor.check_services() - - # Try to get RAG stats if initialized - rag_stats = None - try: - rag = get_rag_engine() - rag_stats = rag.get_stats() - except Exception as e: - logger.warning(f"Could not get RAG stats: {e}") - - return jsonify({ - 'status': 'ok', - 'timestamp': datetime.now().isoformat(), - 'services': services, - 'rag_stats': rag_stats - }) - except Exception as e: - logger.error(f"Error checking status: {e}") - return jsonify({ - 'status': 'error', - 'error': str(e), - 'timestamp': datetime.now().isoformat() - }), 500 - -@app.route('/query', methods=['POST']) -def query_papers(): - """API endpoint to query papers using RAG""" - try: - data = request.get_json() - - if not data or 'query' not in data: - return jsonify({ - 'status': 'error', - 'error': 'No query provided' - }), 400 - - query = data.get('query') - paper_ids = data.get('paper_ids') # Optional: limit to specific papers - n_results = data.get('n_results', 5) - - logger.info(f"Processing query: {query}") - - # Get RAG engine - rag = get_rag_engine() - - # Query the RAG system - result = rag.query( - query=query, - n_results=n_results, - paper_ids=paper_ids - ) - - return jsonify({ - 'status': 'ok', - 'result': result - }) - - except Exception as e: - logger.error(f"Error processing query: {e}") - return jsonify({ - 'status': 'error', - 'error': str(e) - }), 500 - -@app.route('/upload', methods=['POST']) -def upload_paper(): - """API endpoint to upload and ingest a paper into RAG system""" - try: - # Check if file part exists - if 'file' not in request.files: - return jsonify({ - 'status': 'error', - 'error': 'No file part in the request' - }), 400 - - file = request.files['file'] - - # Check if file is selected - if file.filename == '': - return jsonify({ - 'status': 'error', - 'error': 'No file selected' - }), 400 - - # Check if file type is allowed - if not allowed_file(file.filename): - return jsonify({ - 'status': 'error', - 'error': f'Only PDF files are allowed for academic papers' - }), 400 - - # Save file - filename = secure_filename(file.filename) - timestamp = datetime.now().strftime('%Y%m%d%H%M%S') - filename_with_timestamp = f"{timestamp}_{filename}" - file_path = os.path.join(app.config['UPLOAD_FOLDER'], filename_with_timestamp) - - file.save(file_path) - logger.info(f"File saved: {file_path}") - - # Process file with GROBID - paper_data = document_processor.process_file_for_rag(file_path) - - if 'error' in paper_data: - return jsonify({ - 'status': 'error', - 'error': paper_data['error'] - }), 400 - - # Get RAG engine - rag = get_rag_engine() - - # Ingest paper into RAG system - ingest_result = rag.ingest_paper(paper_data) - - if not ingest_result.get('success'): - return jsonify({ - 'status': 'error', - 'error': ingest_result.get('error', 'Failed to ingest paper') - }), 500 - - return jsonify({ - 'status': 'ok', - 'result': { - 'paper_id': ingest_result['paper_id'], - 'title': ingest_result['title'], - 'chunks_processed': ingest_result['chunks_processed'], - 'sections_processed': ingest_result['sections_processed'], - 'filename': filename, - 'upload_date': datetime.now().isoformat() - } - }) - - except Exception as e: - logger.error(f"Error uploading file: {e}") - return jsonify({ - 'status': 'error', - 'error': str(e) - }), 500 - -@app.route('/papers', methods=['GET']) -def list_papers(): - """API endpoint to list all uploaded papers""" - try: - rag = get_rag_engine() - papers = rag.list_papers() - - return jsonify({ - 'status': 'ok', - 'papers': papers, - 'total': len(papers) - }) - - except Exception as e: - logger.error(f"Error listing papers: {e}") - return jsonify({ - 'status': 'error', - 'error': str(e) - }), 500 - -@app.route('/papers/', methods=['DELETE']) -def delete_paper(paper_id): - """API endpoint to delete a paper""" - try: - rag = get_rag_engine() - success = rag.delete_paper(paper_id) - - if success: - return jsonify({ - 'status': 'ok', - 'message': f'Paper {paper_id} deleted successfully' - }) - else: - return jsonify({ - 'status': 'error', - 'error': 'Paper not found or could not be deleted' - }), 404 - - except Exception as e: - logger.error(f"Error deleting paper: {e}") - return jsonify({ - 'status': 'error', - 'error': str(e) - }), 500 - -@app.route('/compare', methods=['POST']) -def compare_papers(): - """API endpoint to compare multiple papers""" - try: - data = request.get_json() - - if not data or 'paper_ids' not in data: - return jsonify({ - 'status': 'error', - 'error': 'No paper IDs provided' - }), 400 - - paper_ids = data.get('paper_ids') - comparison_aspects = data.get('aspects', ['methodology', 'results', 'conclusions', 'limitations']) - - if not isinstance(paper_ids, list) or len(paper_ids) < 2: - return jsonify({ - 'status': 'error', - 'error': 'At least 2 paper IDs required for comparison' - }), 400 - - logger.info(f"Comparing papers: {paper_ids}") - - rag = get_rag_engine() - result = rag.compare_papers( - paper_ids=paper_ids, - comparison_aspects=comparison_aspects - ) - - return jsonify({ - 'status': 'ok', - 'result': result - }) - - except Exception as e: - logger.error(f"Error comparing papers: {e}") - return jsonify({ - 'status': 'error', - 'error': str(e) - }), 500 - -@app.route('/files/', methods=['GET']) -def get_file(filename): - """API endpoint to retrieve an uploaded file""" - try: - return send_from_directory(app.config['UPLOAD_FOLDER'], filename) - except Exception as e: - logger.error(f"Error retrieving file: {e}") - return jsonify({ - 'status': 'error', - 'error': str(e) - }), 404 - -@app.errorhandler(413) -def request_entity_too_large(error): - """Handle file too large error""" - return jsonify({ - 'status': 'error', - 'error': 'File too large' - }), 413 - -@app.errorhandler(404) -def not_found(error): - """Handle 404 error""" - return jsonify({ - 'status': 'error', - 'error': 'Resource not found' - }), 404 - -@app.errorhandler(500) -def internal_server_error(error): - """Handle 500 error""" - return jsonify({ - 'status': 'error', - 'error': 'Internal server error' - }), 500 - -if __name__ == '__main__': - # Get port from environment or use default - port = int(os.environ.get('PORT', 5000)) - - # Get debug mode from environment or use default - debug = os.environ.get('DEBUG', 'False').lower() == 'true' - - # Start server - logger.info(f"Starting server on port {port}, debug={debug}") - app.run(host='0.0.0.0', port=port, debug=debug) \ No newline at end of file diff --git a/apps/backend/backend.log b/apps/backend/backend.log deleted file mode 100644 index c7283d1..0000000 --- a/apps/backend/backend.log +++ /dev/null @@ -1,24 +0,0 @@ -nohup: ignoring input -2025-11-14 00:34:15,838 - text_processor - INFO - Downloading NLTK resources... -[nltk_data] Downloading package punkt to /home/dk/nltk_data... -[nltk_data] Package punkt is already up-to-date! -[nltk_data] Downloading package stopwords to /home/dk/nltk_data... -[nltk_data] Package stopwords is already up-to-date! -[nltk_data] Downloading package wordnet to /home/dk/nltk_data... -[nltk_data] Package wordnet is already up-to-date! -2025-11-14 00:34:15,968 - sentiment_analyzer - INFO - Downloading NLTK resources... -[nltk_data] Downloading package vader_lexicon to /home/dk/nltk_data... -[nltk_data] Package vader_lexicon is already up-to-date! -[nltk_data] Downloading package punkt to /home/dk/nltk_data... -[nltk_data] Package punkt is already up-to-date! -2025-11-14 00:34:15,969 - logging_config - INFO - Logging system initialized -2025-11-14 00:34:15,970 - document_processor - WARNING - Using GROBID URL: http://localhost:8070 -2025-11-14 00:34:15,970 - grobid_client - WARNING - Using GROBID URL: http://localhost:8070 -2025-11-14 00:34:15,971 - grobid_client - ERROR - Unable to connect to GROBID service: http://localhost:8070, error: HTTPConnectionPool(host='localhost', port=8070): Max retries exceeded with url: /api/version (Caused by NewConnectionError(': Failed to establish a new connection: [Errno 111] Connection refused')) -2025-11-14 00:34:15,980 - logging_config - INFO - Starting server on port 5000, debug=False -Created/verified log directory at: /home/dk/code/projects/scholar_sense/apps/backend/logs -Created/verified log directory at: /home/dk/code/projects/scholar_sense/apps/backend/logs - * Serving Flask app 'app' - * Debug mode: off -Address already in use -Port 5000 is in use by another program. Either identify and stop that program, or start the server with a different port. diff --git a/apps/backend/config.py b/apps/backend/config.py new file mode 100755 index 0000000..b350cc8 --- /dev/null +++ b/apps/backend/config.py @@ -0,0 +1,18 @@ +import os +from dotenv import load_dotenv + +load_dotenv() + +class Settings: + GOOGLE_API_KEY: str = os.getenv("GOOGLE_API_KEY", "") + CHROMA_DB_PATH: str = os.getenv("CHROMA_DB_PATH", "./chroma_db") + UPLOAD_DIR: str = os.getenv("UPLOAD_DIR", "./uploads") + CHUNK_SIZE: int = 1000 + CHUNK_OVERLAP: int = 200 + TOP_K_RESULTS: int = 5 + +settings = Settings() + +# Create necessary directories +os.makedirs(settings.UPLOAD_DIR, exist_ok=True) +os.makedirs(settings.CHROMA_DB_PATH, exist_ok=True) diff --git a/apps/backend/document_processor.py b/apps/backend/document_processor.py deleted file mode 100644 index 5559e40..0000000 --- a/apps/backend/document_processor.py +++ /dev/null @@ -1,562 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- - -""" -Document Processing Module -Processes documents using GROBID and extracts structured information for RAG -""" - -import os -import json -import logging -import hashlib -from pathlib import Path -from datetime import datetime -from typing import Dict, Any, List - -from grobid_client import GrobidClient - -# Get logger for this module -logger = logging.getLogger(__name__) - -class DocumentProcessor: - """Document processing class for extracting and analyzing document content""" - - def __init__(self, grobid_url=None): - """ - Initialize document processor - - Parameters: - grobid_url: URL of the GROBID service, if None will use environment variable - """ - # Get GROBID URL from environment variable or use provided value or default - self.grobid_url = grobid_url or os.environ.get("GROBID_URL", "http://localhost:8070") - logger.info(f"Using GROBID URL: {self.grobid_url}") - self.grobid_client = GrobidClient(self.grobid_url) - self.upload_dir = os.environ.get("UPLOAD_DIR", "uploads") - - # Create upload directory if it doesn't exist - os.makedirs(self.upload_dir, exist_ok=True) - - def check_services(self): - """ - Check if all required services are available - - Returns: - Dictionary with service status information - """ - services = {} - - # Check GROBID service - try: - grobid_status = self.grobid_client.check_service() - services["grobid"] = { - "available": grobid_status, - "url": self.grobid_client.base_url - } - except Exception as e: - logger.error(f"Error checking GROBID service: {e}") - services["grobid"] = { - "available": False, - "error": str(e), - "url": self.grobid_client.base_url - } - - return services - - def process_file_for_rag(self, file_path: str) -> Dict[str, Any]: - """ - Process a document file for RAG ingestion - - Parameters: - file_path: Path to the document file - - Returns: - Dictionary with structured paper data ready for RAG ingestion - """ - file_path = Path(file_path) - - if not file_path.exists(): - logger.error(f"File not found: {file_path}") - return {"error": "File not found"} - - # Only support PDF files for academic papers - if file_path.suffix.lower() == '.pdf': - return self._process_pdf_for_rag(file_path) - else: - logger.error(f"Unsupported file type: {file_path.suffix}") - return {"error": f"Only PDF files are supported for academic papers"} - - def _process_pdf_for_rag(self, file_path: Path) -> Dict[str, Any]: - """ - Process a PDF document using GROBID for RAG ingestion - - Parameters: - file_path: Path to the PDF file - - Returns: - Dictionary with structured paper data ready for RAG - """ - logger.info(f"Processing PDF file for RAG: {file_path}") - - try: - # Extract text using GROBID - grobid_result = self.grobid_client.process_fulltext(file_path) - - if not grobid_result: - return {"error": "Failed to process PDF with GROBID"} - - # Generate paper ID from filename and timestamp - paper_id = self._generate_paper_id(file_path.name) - - # Extract structured data - paper_data = { - 'paper_id': paper_id, - 'filename': file_path.name, - 'file_size': file_path.stat().st_size, - 'upload_date': datetime.now().isoformat(), - 'title': grobid_result.get('title', 'Unknown'), - 'authors': grobid_result.get('authors', ''), - 'year': self._extract_year(grobid_result), - 'abstract': grobid_result.get('abstract', ''), - 'sections': self._extract_sections(grobid_result), - 'references': grobid_result.get('references', []) - } - - logger.info(f"Extracted paper: {paper_data['title']}") - logger.info(f"Found {len(paper_data['sections'])} sections") - - return paper_data - - except Exception as e: - logger.error(f"Error processing PDF: {e}") - return {"error": f"Error processing PDF: {str(e)}"} - - def _generate_paper_id(self, filename: str) -> str: - """ - Generate unique paper ID from filename and timestamp - - Parameters: - filename: Original filename - - Returns: - Unique paper ID - """ - timestamp = datetime.now().isoformat() - content = f"{filename}_{timestamp}" - return hashlib.md5(content.encode()).hexdigest() - - def _extract_year(self, grobid_result: Dict[str, Any]) -> str: - """ - Extract publication year from GROBID result - - Parameters: - grobid_result: GROBID processing result - - Returns: - Year string or empty string - """ - # Try to extract year from various fields - # Could be in title, metadata, or references - # This is a simplified version - return grobid_result.get('year', '') - - def _extract_sections(self, grobid_result: Dict[str, Any]) -> List[Dict[str, Any]]: - """ - Extract sections from GROBID result - - Parameters: - grobid_result: GROBID processing result - - Returns: - List of sections with name, text, and page info - """ - sections = [] - - # Add abstract as first section - if grobid_result.get('abstract'): - sections.append({ - 'name': 'Abstract', - 'text': grobid_result['abstract'], - 'page': 1 - }) - - # Add body sections - if grobid_result.get('sections'): - for section in grobid_result['sections']: - if section.get('text'): - sections.append({ - 'name': section.get('heading', 'Body'), - 'text': section.get('text', ''), - 'page': section.get('page', 'N/A') - }) - elif grobid_result.get('body_text'): - # If no sections, use entire body as one section - sections.append({ - 'name': 'Body', - 'text': grobid_result['body_text'], - 'page': 'N/A' - }) - - return sections - - def _process_text_file(self, file_path): - """ - Process a text file - - Parameters: - file_path: Path to the text file - - Returns: - Dictionary with extracted document information - """ - logger.info(f"Processing text file: {file_path}") - - try: - # Read text file - with open(file_path, 'r', encoding='utf-8') as f: - text_content = f.read() - - # Process text - document_data = { - 'full_text': text_content - } - - # Try to extract title (first line) - lines = text_content.strip().split('\n') - if lines: - document_data['title'] = lines[0].strip() - - # If there are more lines, consider the rest as body - if len(lines) > 1: - document_data['body_text'] = '\n'.join(lines[1:]).strip() - - # Analyze document sentiment - sentiment_result = self.sentiment_analyzer.analyze_document(document_data) - - # Extract keywords - keywords = self.text_processor.extract_keywords(text_content, top_n=10) - document_data['keywords'] = [{"word": word, "count": count} for word, count in keywords] - - # Combine results - result = { - "document": document_data, - "sentiment": sentiment_result, - "file_info": { - "filename": file_path.name, - "file_type": "text", - "file_size": file_path.stat().st_size - } - } - - return result - - except Exception as e: - logger.error(f"Error processing text file: {e}") - return {"error": f"Error processing text file: {str(e)}"} - - def _process_json_file(self, file_path): - """ - Process a JSON file - - Parameters: - file_path: Path to the JSON file - - Returns: - Dictionary with extracted document information - """ - logger.info(f"Processing JSON file: {file_path}") - - try: - # Read JSON file - with open(file_path, 'r', encoding='utf-8') as f: - json_data = json.load(f) - - # Check if JSON has expected structure - if not isinstance(json_data, dict): - return {"error": "Invalid JSON format: root must be an object"} - - # Extract document data - document_data = {} - - # Extract title - if json_data.get('title'): - document_data['title'] = json_data['title'] - - # Extract abstract - if json_data.get('abstract'): - document_data['abstract'] = json_data['abstract'] - - # Extract body text - if json_data.get('body_text') or json_data.get('text') or json_data.get('content'): - document_data['body_text'] = json_data.get('body_text') or json_data.get('text') or json_data.get('content') - - # Extract authors - if json_data.get('authors'): - document_data['authors'] = json_data['authors'] - - # Combine all text for full-text analysis - full_text_parts = [] - if document_data.get('title'): - full_text_parts.append(document_data['title']) - if document_data.get('abstract'): - full_text_parts.append(document_data['abstract']) - if document_data.get('body_text'): - full_text_parts.append(document_data['body_text']) - - document_data['full_text'] = ' '.join(full_text_parts) - - # If no document parts were found, use the entire JSON as full text - if not document_data.get('full_text'): - document_data['full_text'] = json.dumps(json_data) - - # Analyze document sentiment - sentiment_result = self.sentiment_analyzer.analyze_document(document_data) - - # Extract keywords - keywords = self.text_processor.extract_keywords(document_data['full_text'], top_n=10) - document_data['keywords'] = [{"word": word, "count": count} for word, count in keywords] - - # Combine results - result = { - "document": document_data, - "sentiment": sentiment_result, - "file_info": { - "filename": file_path.name, - "file_type": "json", - "file_size": file_path.stat().st_size - } - } - - return result - - except Exception as e: - logger.error(f"Error processing JSON file: {e}") - return {"error": f"Error processing JSON file: {str(e)}"} - - def process_text(self, text, title=None): - """ - Process text content directly - - Parameters: - text: Text content to process - title: Optional title for the text - - Returns: - Dictionary with extracted document information - """ - logger.info("Processing text content") - - try: - # Create document data - document_data = { - 'full_text': text - } - - if title: - document_data['title'] = title - - # Extract sentences - sentences = self.text_processor.extract_sentences(text) - - # If no title provided, use first sentence as title - if not title and sentences: - document_data['title'] = sentences[0] - # Use remaining sentences as body text - if len(sentences) > 1: - document_data['body_text'] = ' '.join(sentences[1:]) - else: - document_data['body_text'] = text - - # Analyze document sentiment - sentiment_result = self.sentiment_analyzer.analyze_document(document_data) - - # Extract keywords - keywords = self.text_processor.extract_keywords(text, top_n=10) - document_data['keywords'] = [{"word": word, "count": count} for word, count in keywords] - - # Combine results - result = { - "document": document_data, - "sentiment": sentiment_result, - "file_info": { - "file_type": "text", - "text_length": len(text) - } - } - - return result - - except Exception as e: - logger.error(f"Error processing text content: {e}") - return {"error": f"Error processing text content: {str(e)}"} - - def save_uploaded_file(self, file_data, filename): - """ - Save uploaded file to disk - - Parameters: - file_data: File data (bytes) - filename: Name of the file - - Returns: - Path to the saved file - """ - try: - # Ensure filename is safe - safe_filename = os.path.basename(filename) - file_path = os.path.join(self.upload_dir, safe_filename) - - # Write file to disk - with open(file_path, 'wb') as f: - f.write(file_data) - - logger.info(f"File saved: {file_path}") - return file_path - - except Exception as e: - logger.error(f"Error saving file: {e}") - raise - - def process_file_with_topic(self, file_path, topic): - """ - Process a document file with respect to a specific topic - - Parameters: - file_path: Path to the document file - topic: Topic to analyze the document against - - Returns: - Dictionary with topic-based analysis results - """ - file_path = Path(file_path) - - if not file_path.exists(): - logger.error(f"File not found: {file_path}") - return {"error": "File not found"} - - try: - logger.info(f"Processing file with topic: {topic}, file: {file_path}") - - # First process the document normally (this will use GROBID for PDF files) - doc_result = self.process_file(file_path) - - if "error" in doc_result: - logger.error(f"Error in document processing: {doc_result['error']}") - return doc_result - - # Get the full text from the document - full_text = doc_result["document"].get("full_text", "") - - if not full_text: - logger.warning(f"Empty text extracted from file: {file_path}") - return {"error": "No text could be extracted from the document"} - - logger.info(f"Successfully extracted text from document, length: {len(full_text)} characters") - - # Extract topic-related sentences - topic_sentences = self.text_processor.extract_topic_sentences(full_text, topic) - logger.info(f"Found {len(topic_sentences)} sentences related to topic '{topic}'") - - # If no topic-related sentences found, use the full text - if not topic_sentences: - logger.info(f"No sentences related to topic '{topic}' found. Using full text for analysis.") - topic_sentences = self.text_processor.extract_sentences(full_text)[:10] # Use first 10 sentences - logger.info(f"Using first {len(topic_sentences)} sentences from document") - - # Analyze sentiment for topic-related content - topic_text = " ".join(topic_sentences) - logger.info(f"Analyzing sentiment for topic text, length: {len(topic_text)} characters") - topic_sentiment = self.sentiment_analyzer.analyze_text(topic_text) - - # Calculate topic relevance score - relevance_score = self.text_processor.calculate_topic_relevance(full_text, topic) - logger.info(f"Topic relevance score: {relevance_score}") - - # Check if relevance score is too low - if relevance_score < 0.05: - logger.info(f"Relevance score too low: {relevance_score}, not proceeding with analysis") - return { - "document": doc_result["document"], - "topic_analysis": { - "topic": topic, - "relevance_score": relevance_score, - "low_relevance": True, - "message": "This topic is not associated with the uploaded document and cannot be analyzed. Please try another related topic" - }, - "file_info": doc_result["file_info"] - } - - # Extract topic-specific keywords - topic_keywords = self.text_processor.extract_topic_keywords(full_text, topic) - - # If no topic-specific keywords found, use general keywords - if not topic_keywords: - logger.info(f"No keywords related to topic '{topic}' found. Using general keywords.") - topic_keywords = self.text_processor.extract_keywords(full_text) - - # Determine stance (support/oppose/neutral) - stance = self._determine_stance(topic_sentiment, relevance_score) - logger.info(f"Determined stance: {stance}") - - # Combine results - result = { - "document": doc_result["document"], - "topic_analysis": { - "topic": topic, - "stance": stance, - "relevance_score": relevance_score, - "sentiment": topic_sentiment, - "topic_sentences": topic_sentences, - "topic_keywords": topic_keywords - }, - "file_info": doc_result["file_info"] - } - - return result - - except Exception as e: - logger.error(f"Error processing file with topic: {e}") - return {"error": f"Error processing file with topic: {str(e)}"} - - def _determine_stance(self, sentiment_result, relevance_score): - """ - Determine the stance of the document regarding the topic - - Parameters: - sentiment_result: Sentiment analysis result - relevance_score: Topic relevance score - - Returns: - String indicating the stance: 'support', 'oppose', or 'neutral' - """ - if relevance_score < 0.3: # Low relevance threshold - return "neutral" - - sentiment_score = sentiment_result.get("score", 0) - - if sentiment_score > 0.2: # Positive sentiment threshold - return "support" - elif sentiment_score < -0.2: # Negative sentiment threshold - return "oppose" - else: - return "neutral" - -# Test code -if __name__ == "__main__": - processor = DocumentProcessor() - - # Check services - services = processor.check_services() - print("Services status:", services) - - # Test with a sample text - test_text = """ - Natural language processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence - concerned with the interactions between computers and human language, in particular how to program computers - to process and analyze large amounts of natural language data. The goal is a computer capable of "understanding" - the contents of documents, including the contextual nuances of the language within them. - """ - - result = processor.process_text(test_text, title="Introduction to NLP") - print(json.dumps(result, indent=2)) \ No newline at end of file diff --git a/apps/backend/embeddings.py b/apps/backend/embeddings.py deleted file mode 100644 index cb5dae6..0000000 --- a/apps/backend/embeddings.py +++ /dev/null @@ -1,124 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- - -""" -Embeddings Module -Generates embeddings using sentence transformers (BGE-large-en) -""" - -import os -import logging -from typing import List, Union -from sentence_transformers import SentenceTransformer - -logger = logging.getLogger(__name__) - - -class EmbeddingGenerator: - """Generates embeddings using sentence transformers""" - - def __init__(self, model_name: str = "BAAI/bge-large-en-v1.5"): - """ - Initialize embedding generator - - Parameters: - model_name: Name of the sentence transformer model to use - Default: BGE-large-en-v1.5 (best for retrieval) - """ - self.model_name = model_name - logger.info(f"Loading embedding model: {model_name}") - - try: - self.model = SentenceTransformer(model_name) - self.embedding_dim = self.model.get_sentence_embedding_dimension() - logger.info(f"Model loaded successfully. Embedding dimension: {self.embedding_dim}") - except Exception as e: - logger.error(f"Error loading embedding model: {e}") - raise - - def encode_text(self, text: Union[str, List[str]], batch_size: int = 32) -> Union[List[float], List[List[float]]]: - """ - Generate embeddings for text or list of texts - - Parameters: - text: Single text string or list of text strings - batch_size: Batch size for processing multiple texts - - Returns: - Embedding vector(s) as list or list of lists - """ - try: - # Handle single text - if isinstance(text, str): - embedding = self.model.encode(text, convert_to_numpy=True) - return embedding.tolist() - - # Handle list of texts - elif isinstance(text, list): - embeddings = self.model.encode( - text, - batch_size=batch_size, - show_progress_bar=len(text) > 100, - convert_to_numpy=True - ) - return embeddings.tolist() - - else: - raise ValueError(f"Input must be string or list of strings, got {type(text)}") - - except Exception as e: - logger.error(f"Error generating embeddings: {e}") - raise - - def encode_query(self, query: str) -> List[float]: - """ - Generate embedding for a query (adds instruction prefix for BGE models) - - Parameters: - query: Query text - - Returns: - Query embedding vector - """ - try: - # BGE models perform better with instruction prefix for queries - if "bge" in self.model_name.lower(): - query = f"Represent this sentence for searching relevant passages: {query}" - - embedding = self.model.encode(query, convert_to_numpy=True) - return embedding.tolist() - - except Exception as e: - logger.error(f"Error generating query embedding: {e}") - raise - - def get_embedding_dimension(self) -> int: - """ - Get the dimension of embeddings produced by this model - - Returns: - Embedding dimension - """ - return self.embedding_dim - - -# Global embedding generator instance (lazy loaded) -_embedding_generator = None - - -def get_embedding_generator(model_name: str = "BAAI/bge-large-en-v1.5") -> EmbeddingGenerator: - """ - Get or create global embedding generator instance - - Parameters: - model_name: Name of the model to use - - Returns: - EmbeddingGenerator instance - """ - global _embedding_generator - - if _embedding_generator is None: - _embedding_generator = EmbeddingGenerator(model_name) - - return _embedding_generator diff --git a/apps/backend/grobid_client.py b/apps/backend/grobid_client.py deleted file mode 100644 index 63de7d6..0000000 --- a/apps/backend/grobid_client.py +++ /dev/null @@ -1,211 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- - -""" -GROBID Client Module -For interacting with GROBID service to extract text content from PDF documents -""" - -import os -import requests -import logging -import xml.etree.ElementTree as ET -from pathlib import Path - -# Configure logging -logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s') -logger = logging.getLogger(__name__) - -class GrobidClient: - """GROBID API Client""" - - def __init__(self, base_url=None): - """ - Initialize GROBID client - - Parameters: - base_url: Base URL of the GROBID service, if None will use environment variable - """ - # Get base URL from environment variable or use provided value or default - self.base_url = base_url or os.environ.get("GROBID_URL", "http://localhost:8070") - logger.warning(f"Using GROBID URL: {self.base_url}") - self.check_service() - - def check_service(self): - """Check if GROBID service is available""" - try: - response = requests.get(f"{self.base_url}/api/version", timeout=5) - if response.status_code == 200: - logger.info(f"GROBID service available, version: {response.text}") - return True - else: - logger.warning(f"GROBID service returned error: {response.status_code}") - return False - except requests.exceptions.RequestException as e: - logger.error(f"Unable to connect to GROBID service: {self.base_url}, error: {e}") - return False - - def process_fulltext(self, pdf_path): - """ - Process full text of PDF document - - Parameters: - pdf_path: Path to PDF file - - Returns: - Extracted text content dictionary, including title, abstract, body text, etc. - """ - logger.info(f"Processing document full text: {pdf_path}") - url = f"{self.base_url}/api/processFulltextDocument" - - try: - with open(pdf_path, 'rb') as pdf_file: - files = {'input': (os.path.basename(pdf_path), pdf_file, 'application/pdf')} - response = requests.post(url, files=files, timeout=120) - - if response.status_code != 200: - logger.error(f"Processing failed: {response.status_code}") - return None - - # Parse XML response - return self._parse_tei_response(response.text) - - except Exception as e: - logger.error(f"Error processing document: {e}") - return None - - def _parse_tei_response(self, tei_content): - """ - Parse TEI XML response - - Parameters: - tei_content: TEI XML content - - Returns: - Parsed text content dictionary with sections - """ - try: - # Register TEI namespace - ns = {'tei': 'http://www.tei-c.org/ns/1.0'} - - # Parse XML - root = ET.fromstring(tei_content) - - # Extract title - title_elem = root.find('.//tei:titleStmt/tei:title', ns) - title = title_elem.text if title_elem is not None else "" - - # Extract authors - author_elems = root.findall('.//tei:fileDesc/tei:sourceDesc//tei:author/tei:persName', ns) - authors = [] - for author in author_elems: - forename = author.find('.//tei:forename', ns) - surname = author.find('.//tei:surname', ns) - name_parts = [] - if forename is not None and forename.text: - name_parts.append(forename.text) - if surname is not None and surname.text: - name_parts.append(surname.text) - if name_parts: - authors.append(' '.join(name_parts)) - authors_str = ', '.join(authors) if authors else '' - - # Extract abstract - abstract_elems = root.findall('.//tei:profileDesc/tei:abstract//tei:p', ns) - abstract = ' '.join([self._get_element_text(elem) for elem in abstract_elems if elem is not None]) - - # Extract sections from body - sections = [] - body = root.find('.//tei:body', ns) - if body is not None: - divs = body.findall('.//tei:div', ns) - for div in divs: - # Get section heading - head_elem = div.find('.//tei:head', ns) - heading = self._get_element_text(head_elem) if head_elem is not None else 'Body' - - # Get all paragraphs in this section - p_elems = div.findall('.//tei:p', ns) - section_text = ' '.join([self._get_element_text(p) for p in p_elems if p is not None]) - - if section_text: - sections.append({ - 'heading': heading, - 'text': section_text, - 'page': 'N/A' # GROBID doesn't always provide page numbers - }) - - # If no sections found, use all body text as one section - if not sections: - body_elems = root.findall('.//tei:body//tei:p', ns) - body_text = ' '.join([self._get_element_text(elem) for elem in body_elems if elem is not None]) - if body_text: - sections.append({ - 'heading': 'Body', - 'text': body_text, - 'page': 'N/A' - }) - - # Extract references - ref_elems = root.findall('.//tei:listBibl/tei:biblStruct', ns) - references = [] - for ref in ref_elems: - ref_title = ref.find('.//tei:title', ns) - if ref_title is not None and ref_title.text: - references.append(ref_title.text) - - return { - 'title': title, - 'authors': authors_str, - 'abstract': abstract, - 'sections': sections, - 'references': references, - 'body_text': ' '.join([s['text'] for s in sections]), # Keep for backward compatibility - 'full_text': f"{title} {abstract} {' '.join([s['text'] for s in sections])}" - } - - except Exception as e: - logger.error(f"Error parsing TEI response: {e}") - return { - 'title': "", - 'authors': "", - 'abstract': "", - 'sections': [], - 'body_text': "", - 'references': [], - 'full_text': "" - } - - def _get_element_text(self, element): - """ - Extract all text from an element and its children - - Parameters: - element: XML element - - Returns: - Combined text content - """ - if element is None: - return "" - - text_parts = [] - if element.text: - text_parts.append(element.text) - - for child in element: - child_text = self._get_element_text(child) - if child_text: - text_parts.append(child_text) - if child.tail: - text_parts.append(child.tail) - - return ' '.join(text_parts) - -# Test code -if __name__ == "__main__": - client = GrobidClient() - if client.check_service(): - print("GROBID service available") - else: - print("GROBID service not available") \ No newline at end of file diff --git a/apps/backend/grobid_deployment/deploy-grobid.sh b/apps/backend/grobid_deployment/deploy-grobid.sh deleted file mode 100755 index 80afc30..0000000 --- a/apps/backend/grobid_deployment/deploy-grobid.sh +++ /dev/null @@ -1,166 +0,0 @@ -#!/bin/bash - -# Script for deploying GROBID -# Author: Claude -# Date: 2024 - -# Color definitions -GREEN='\033[0;32m' -YELLOW='\033[1;33m' -RED='\033[0;31m' -NC='\033[0m' # No color - -# Print colored messages -print_message() { - echo -e "${GREEN}[INFO]${NC} $1" -} - -print_warning() { - echo -e "${YELLOW}[WARNING]${NC} $1" -} - -print_error() { - echo -e "${RED}[ERROR]${NC} $1" -} - -# Check if Docker is installed -check_docker() { - if ! command -v docker &> /dev/null; then - print_error "Docker is not installed. Please install Docker first: https://docs.docker.com/get-docker/" - exit 1 - fi - print_message "Docker is installed." -} - -# Check if GPU support is available -check_gpu() { - if command -v nvidia-smi &> /dev/null; then - print_message "NVIDIA GPU detected." - HAS_GPU=true - else - print_warning "No NVIDIA GPU detected or NVIDIA drivers not installed. Will use CPU mode." - HAS_GPU=false - fi -} - -# Deploy GROBID -deploy_grobid() { - local version="0.8.1" - local port="8070" - local host_port="8070" - local image_type="light" - local detached=false - - # Parse command line arguments - while [[ $# -gt 0 ]]; do - case $1 in - --version) - version="$2" - shift 2 - ;; - --port) - host_port="$2" - shift 2 - ;; - --type) - image_type="$2" - shift 2 - ;; - --detached|-d) - detached=true - shift - ;; - *) - print_error "Unknown parameter: $1" - exit 1 - ;; - esac - done - - # Choose Docker image based on image type - if [[ "$image_type" == "full" ]]; then - IMAGE="grobid/grobid:${version}" - print_message "Using full version image (includes deep learning models)" - elif [[ "$image_type" == "light" ]]; then - IMAGE="lfoppiano/grobid:${version}" - print_message "Using lightweight image (CRF models only)" - else - print_error "Invalid image type: ${image_type}. Please use 'full' or 'light'" - exit 1 - fi - - # Pull Docker image - print_message "Pulling ${IMAGE} image..." - docker pull ${IMAGE} - - # Build Docker run command - DOCKER_CMD="docker run --rm --init --ulimit core=0" - - # Add GPU support (if available) - if [[ "$HAS_GPU" == true && "$image_type" == "full" ]]; then - DOCKER_CMD="${DOCKER_CMD} --gpus all" - fi - - # Add port mapping - DOCKER_CMD="${DOCKER_CMD} -p ${host_port}:${port}" - - # Add health check port - health_port=$((host_port + 1)) - DOCKER_CMD="${DOCKER_CMD} -p ${health_port}:8071" - - # Add background running parameter - if [[ "$detached" == true ]]; then - DOCKER_CMD="${DOCKER_CMD} -d" - fi - - # Add image name - DOCKER_CMD="${DOCKER_CMD} ${IMAGE}" - - # Run Docker container - print_message "Starting GROBID container..." - print_message "Executing command: ${DOCKER_CMD}" - eval ${DOCKER_CMD} - - # If successfully started, display access information - if [[ $? -eq 0 && "$detached" == true ]]; then - print_message "GROBID has been successfully started in the background!" - print_message "Access GROBID: http://localhost:${host_port}" - print_message "Health check: http://localhost:${health_port}" - elif [[ $? -eq 0 ]]; then - print_message "GROBID has been successfully started!" - print_message "Access in browser: http://localhost:${host_port}" - else - print_error "Failed to start GROBID. Please check the error messages." - fi -} - -# Main function -main() { - print_message "Starting GROBID deployment..." - check_docker - check_gpu - deploy_grobid "$@" -} - -# Show help information -show_help() { - echo "Usage: $0 [options]" - echo "" - echo "Options:" - echo " --version VERSION Specify GROBID version (default: 0.8.1)" - echo " --port PORT Specify host port (default: 8070)" - echo " --type TYPE Specify image type: 'full' or 'light' (default: full)" - echo " --detached, -d Run container in background" - echo "" - echo "Examples:" - echo " $0 --type light --port 8070 -d Run lightweight GROBID in background, mapped to port 8070" -} - -# If no arguments or help argument, show help information -if [[ $# -eq 0 || "$1" == "--help" || "$1" == "-h" ]]; then - show_help - exit 0 -fi - -# Run main function -main "$@" \ No newline at end of file diff --git a/apps/backend/grobid_deployment/grobid-api-example.py b/apps/backend/grobid_deployment/grobid-api-example.py deleted file mode 100644 index 74d827b..0000000 --- a/apps/backend/grobid_deployment/grobid-api-example.py +++ /dev/null @@ -1,171 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- - -""" -GROBID API Usage Example -This script demonstrates how to use GROBID's REST API to process PDF documents -""" - -import os -import sys -import json -import requests -from pathlib import Path - - -class GrobidClient: - """GROBID API Client""" - - def __init__(self, base_url="http://localhost:8070"): - """ - Initialize GROBID client - - Parameters: - base_url: Base URL of the GROBID service - """ - self.base_url = base_url - # Check if GROBID service is available - try: - response = requests.get(f"{self.base_url}/api/version") - if response.status_code == 200: - print(f"GROBID service available, version: {response.text}") - else: - print(f"GROBID service returned error: {response.status_code}") - except requests.exceptions.ConnectionError: - print(f"Unable to connect to GROBID service: {self.base_url}") - print("Please make sure the GROBID service is running") - sys.exit(1) - - def process_header(self, pdf_path, output_path=None): - """ - Process the header section of a PDF document - - Parameters: - pdf_path: Path to the PDF file - output_path: Output file path (optional) - - Returns: - Processed TEI XML text - """ - print(f"Processing document header: {pdf_path}") - url = f"{self.base_url}/api/processHeaderDocument" - - with open(pdf_path, 'rb') as pdf_file: - files = {'input': (os.path.basename(pdf_path), pdf_file, 'application/pdf')} - response = requests.post(url, files=files) - - if response.status_code != 200: - print(f"Processing failed: {response.status_code}") - return None - - if output_path: - with open(output_path, 'w', encoding='utf-8') as out_file: - out_file.write(response.text) - print(f"Header information saved to: {output_path}") - - return response.text - - def process_fulltext(self, pdf_path, output_path=None): - """ - Process the full text of a PDF document - - Parameters: - pdf_path: Path to the PDF file - output_path: Output file path (optional) - - Returns: - Processed TEI XML text - """ - print(f"Processing document full text: {pdf_path}") - url = f"{self.base_url}/api/processFulltextDocument" - - with open(pdf_path, 'rb') as pdf_file: - files = {'input': (os.path.basename(pdf_path), pdf_file, 'application/pdf')} - response = requests.post(url, files=files) - - if response.status_code != 200: - print(f"Processing failed: {response.status_code}") - return None - - if output_path: - with open(output_path, 'w', encoding='utf-8') as out_file: - out_file.write(response.text) - print(f"Full text information saved to: {output_path}") - - return response.text - - def process_references(self, pdf_path, output_path=None): - """ - Process references in a PDF document - - Parameters: - pdf_path: Path to the PDF file - output_path: Output file path (optional) - - Returns: - Processed TEI XML text - """ - print(f"Processing document references: {pdf_path}") - url = f"{self.base_url}/api/processReferences" - - with open(pdf_path, 'rb') as pdf_file: - files = {'input': (os.path.basename(pdf_path), pdf_file, 'application/pdf')} - response = requests.post(url, files=files) - - if response.status_code != 200: - print(f"Processing failed: {response.status_code}") - return None - - if output_path: - with open(output_path, 'w', encoding='utf-8') as out_file: - out_file.write(response.text) - print(f"References information saved to: {output_path}") - - return response.text - - -def main(): - """Main function""" - if len(sys.argv) < 2: - print("Usage: python grobid-api-example.py [grobid_service_URL]") - sys.exit(1) - - pdf_path = sys.argv[1] - if not os.path.exists(pdf_path): - print(f"Error: File does not exist: {pdf_path}") - sys.exit(1) - - # Check if file is a PDF - if not pdf_path.lower().endswith('.pdf'): - print(f"Warning: File may not be a PDF: {pdf_path}") - - # Set GROBID service URL - grobid_url = sys.argv[2] if len(sys.argv) > 2 else "http://localhost:8070" - - # Create output directory - output_dir = Path("grobid_output") - output_dir.mkdir(exist_ok=True) - - # Get filename (without extension) - filename = Path(pdf_path).stem - - # Initialize GROBID client - client = GrobidClient(grobid_url) - - # Process document header - header_output = output_dir / f"{filename}_header.xml" - client.process_header(pdf_path, header_output) - - # Process document full text - fulltext_output = output_dir / f"{filename}_fulltext.xml" - client.process_fulltext(pdf_path, fulltext_output) - - # Process references - references_output = output_dir / f"{filename}_references.xml" - client.process_references(pdf_path, references_output) - - print("\nProcessing complete! Output files saved in 'grobid_output' directory") - - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/apps/backend/grobid_deployment/grobid-docker-deployment.md b/apps/backend/grobid_deployment/grobid-docker-deployment.md deleted file mode 100644 index 4032584..0000000 --- a/apps/backend/grobid_deployment/grobid-docker-deployment.md +++ /dev/null @@ -1,88 +0,0 @@ -# Deploying GROBID with Docker - -GROBID is a machine learning software for extracting information from scholarly documents. This guide will help you deploy GROBID using Docker. - -## Prerequisites - -- Docker installed ([Docker Installation Guide](https://docs.docker.com/get-docker/)) -- If you want to use GPU acceleration (recommended for the full version image), make sure NVIDIA Docker support is installed - -## Deployment Options - -GROBID provides two official Docker images: - -### 1. Full Version Image (about 10GB) - -This image provides the best accuracy, including all required Python and TensorFlow libraries, GPU support, and all deep learning model resources. Use this version if you have a limited number of PDF files to process, have a good performance machine, and prioritize accuracy. - -```bash -# Pull the latest full version image -docker pull grobid/grobid:0.8.1 - -# Run container (with GPU) -docker run --rm --gpus all --init --ulimit core=0 -p 8070:8070 grobid/grobid:0.8.1 - -# Run container (without GPU) -docker run --rm --init --ulimit core=0 -p 8070:8070 grobid/grobid:0.8.1 -``` - -### 2. Lightweight Image (about 300MB) - -This image provides the best runtime performance, memory usage, and Docker image size. However, it doesn't use some models that perform best in terms of accuracy. Use this version if you have a large number of PDF files to process, limited system resources, and accuracy is not as important. - -```bash -# Pull the latest lightweight image -docker pull lfoppiano/grobid:0.8.1 - -# Run container -docker run --rm --init --ulimit core=0 -p 8070:8070 lfoppiano/grobid:0.8.1 -``` - -## Port Mapping - -By default, GROBID runs on port `8070` inside the container. You can map it to any port on the host, such as the traditional `8080` port: - -```bash -# Map container port 8070 to host port 8080 -docker run --rm --init --ulimit core=0 -p 8080:8070 lfoppiano/grobid:0.8.1 - -# Also map health check port -docker run --rm --init --ulimit core=0 -p 8080:8070 -p 8081:8071 lfoppiano/grobid:0.8.1 -``` - -## Accessing GROBID Service - -After deployment, you can access GROBID through: - -- Open a browser and visit `http://localhost:8070` (or your mapped port, such as `http://localhost:8080`) -- Health check can be accessed via `http://localhost:8071` (or your mapped port, such as `http://localhost:8081`) - -## Using GROBID Web Services - -GROBID provides various Web service APIs that you can use through HTTP requests. Detailed API documentation can be found in the GROBID service console. - -Common services include: - -- `/api/processHeaderDocument`: Process document headers -- `/api/processFulltextDocument`: Process full text -- `/api/processReferences`: Process references - -## Data Persistence (Optional) - -If you need to persist GROBID data or configuration, you can use volume mounts: - -```bash -# Mount configuration directory -docker run --rm --init --ulimit core=0 -p 8070:8070 -v /path/to/local/config:/opt/grobid/config lfoppiano/grobid:0.8.1 -``` - -## Troubleshooting - -- If you encounter out-of-memory issues when running on MacOS, increase the default memory limit for Docker containers -- Make sure your system has sufficient resources to run GROBID, especially when using the full version image - -## References - -- [GROBID Official Documentation](https://grobid.readthedocs.io/) -- [GROBID GitHub Repository](https://github.com/kermitt2/grobid) -- [GROBID Docker Documentation](https://grobid.readthedocs.io/en/latest/Grobid-docker/) \ No newline at end of file diff --git a/apps/backend/llm_client.py b/apps/backend/llm_client.py deleted file mode 100644 index 55af568..0000000 --- a/apps/backend/llm_client.py +++ /dev/null @@ -1,310 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- - -""" -LLM Client Module -Integrates with Google Gemini API for generating citation-grounded answers -""" - -import os -import logging -from typing import List, Dict, Any -import google.generativeai as genai - -logger = logging.getLogger(__name__) - - -class LLMClient: - """Client for Google Gemini API""" - - def __init__(self, api_key: str = None, model_name: str = "gemini-pro"): - """ - Initialize Gemini LLM client - - Parameters: - api_key: Google API key (if None, reads from GEMINI_API_KEY env var) - model_name: Name of the Gemini model to use - """ - self.api_key = api_key or os.environ.get("GEMINI_API_KEY") - - if not self.api_key: - raise ValueError("GEMINI_API_KEY not found in environment variables or provided") - - self.model_name = model_name - - # Configure the API - genai.configure(api_key=self.api_key) - - # Initialize the model - self.model = genai.GenerativeModel(model_name) - - logger.info(f"Initialized Gemini LLM client with model: {model_name}") - - def generate_answer( - self, - query: str, - contexts: List[Dict[str, Any]], - temperature: float = 0.3, - max_tokens: int = 1024 - ) -> Dict[str, Any]: - """ - Generate citation-grounded answer from query and retrieved contexts - - Parameters: - query: User's question - contexts: List of retrieved context chunks with metadata - temperature: Sampling temperature (0-1, lower = more deterministic) - max_tokens: Maximum tokens in response - - Returns: - Dictionary with answer, citations, and metadata - """ - try: - # Build context string with citations - context_parts = [] - for i, ctx in enumerate(contexts, 1): - metadata = ctx.get('metadata', {}) - text = ctx.get('text', '') - - paper_title = metadata.get('title', 'Unknown') - section = metadata.get('section', 'Unknown') - page = metadata.get('page', 'N/A') - - context_parts.append( - f"[{i}] Source: {paper_title}, Section: {section}, Page: {page}\n{text}\n" - ) - - context_str = "\n".join(context_parts) - - # Build prompt - prompt = self._build_rag_prompt(query, context_str) - - # Generate response - generation_config = genai.types.GenerationConfig( - temperature=temperature, - max_output_tokens=max_tokens, - ) - - response = self.model.generate_content( - prompt, - generation_config=generation_config - ) - - answer = response.text - - # Extract citations from answer - citations = self._extract_citations(answer, contexts) - - return { - 'answer': answer, - 'citations': citations, - 'model': self.model_name, - 'contexts_used': len(contexts) - } - - except Exception as e: - logger.error(f"Error generating answer: {e}") - return { - 'answer': f"Error generating answer: {str(e)}", - 'citations': [], - 'model': self.model_name, - 'error': str(e) - } - - def _build_rag_prompt(self, query: str, contexts: str) -> str: - """ - Build prompt for RAG-based question answering - - Parameters: - query: User's question - contexts: Formatted context strings with citations - - Returns: - Formatted prompt string - """ - prompt = f"""You are an expert academic research assistant. Your task is to answer questions about academic papers based on the provided context. - -Instructions: -1. Answer the question using ONLY the information provided in the contexts below -2. If the contexts don't contain enough information to answer fully, state what information is missing -3. Always cite your sources using [number] notation matching the context numbers -4. Be precise and academic in your language -5. If multiple papers discuss the topic, compare and contrast their approaches -6. Highlight any disagreements or different perspectives between papers - -Contexts: -{contexts} - -Question: {query} - -Answer:""" - - return prompt - - def _extract_citations(self, answer: str, contexts: List[Dict[str, Any]]) -> List[Dict[str, Any]]: - """ - Extract citation information from answer - - Parameters: - answer: Generated answer text - contexts: Original contexts with metadata - - Returns: - List of citations found in the answer - """ - citations = [] - - for i, ctx in enumerate(contexts, 1): - # Check if citation [i] appears in answer - if f"[{i}]" in answer: - metadata = ctx.get('metadata', {}) - citations.append({ - 'citation_number': i, - 'paper_title': metadata.get('title', 'Unknown'), - 'section': metadata.get('section', 'Unknown'), - 'page': metadata.get('page', 'N/A'), - 'paper_id': metadata.get('paper_id', '') - }) - - return citations - - def generate_comparison( - self, - papers_data: List[Dict[str, Any]], - comparison_aspects: List[str] = None, - temperature: float = 0.3 - ) -> Dict[str, Any]: - """ - Generate multi-paper comparison - - Parameters: - papers_data: List of paper data with chunks and metadata - comparison_aspects: Specific aspects to compare (methods, results, etc.) - temperature: Sampling temperature - - Returns: - Comparison result dictionary - """ - try: - if comparison_aspects is None: - comparison_aspects = ["methodology", "results", "conclusions", "limitations"] - - # Build comparison prompt - papers_summary = [] - for i, paper in enumerate(papers_data, 1): - title = paper.get('title', f'Paper {i}') - abstract = paper.get('abstract', 'No abstract available') - papers_summary.append(f"Paper {i}: {title}\nAbstract: {abstract}\n") - - papers_str = "\n".join(papers_summary) - aspects_str = ", ".join(comparison_aspects) - - prompt = f"""You are comparing multiple academic papers. Analyze and compare the following papers across these aspects: {aspects_str}. - -Papers: -{papers_str} - -Provide a structured comparison highlighting: -1. Similarities in approaches or findings -2. Differences in methodology or conclusions -3. Complementary insights -4. Contradictions or disagreements -5. Overall synthesis - -Comparison:""" - - generation_config = genai.types.GenerationConfig( - temperature=temperature, - max_output_tokens=2048, - ) - - response = self.model.generate_content( - prompt, - generation_config=generation_config - ) - - return { - 'comparison': response.text, - 'papers_compared': len(papers_data), - 'aspects': comparison_aspects, - 'model': self.model_name - } - - except Exception as e: - logger.error(f"Error generating comparison: {e}") - return { - 'comparison': f"Error generating comparison: {str(e)}", - 'error': str(e) - } - - def detect_stance(self, text: str, topic: str = None) -> Dict[str, Any]: - """ - Detect stance in text (supportive, critical, neutral, uncertain) - - Parameters: - text: Text to analyze - topic: Optional topic to analyze stance towards - - Returns: - Stance detection result - """ - try: - topic_str = f" towards '{topic}'" if topic else "" - - prompt = f"""Analyze the stance expressed in the following text{topic_str}. - -Classify the stance as one of: -- SUPPORTIVE: Expresses positive view, agreement, or support -- CRITICAL: Expresses negative view, disagreement, or criticism -- NEUTRAL: Presents balanced view without clear position -- UNCERTAIN: Expresses doubt, lack of evidence, or need for more research - -Provide: -1. Stance classification -2. Confidence score (0-1) -3. Key phrases that indicate this stance -4. Brief explanation - -Text: {text} - -Analysis:""" - - response = self.model.generate_content(prompt) - - # Parse response (simplified - could use more structured parsing) - result_text = response.text - - return { - 'stance_analysis': result_text, - 'text': text[:200] + '...' if len(text) > 200 else text - } - - except Exception as e: - logger.error(f"Error detecting stance: {e}") - return { - 'stance_analysis': f"Error: {str(e)}", - 'error': str(e) - } - - -# Global LLM client instance (lazy loaded) -_llm_client = None - - -def get_llm_client(api_key: str = None, model_name: str = "gemini-pro") -> LLMClient: - """ - Get or create global LLM client instance - - Parameters: - api_key: Google API key - model_name: Model name - - Returns: - LLMClient instance - """ - global _llm_client - - if _llm_client is None: - _llm_client = LLMClient(api_key, model_name) - - return _llm_client diff --git a/apps/backend/logging_config.py b/apps/backend/logging_config.py deleted file mode 100644 index 6d5daf0..0000000 --- a/apps/backend/logging_config.py +++ /dev/null @@ -1,51 +0,0 @@ -import os -import logging -import sys - -def setup_logging(): - """Configure logging for the application""" - # Get the absolute path of the backend directory - BACKEND_DIR = os.path.dirname(os.path.abspath(__file__)) - LOG_DIR = os.path.join(BACKEND_DIR, 'logs') - - try: - # Create logs directory with full permissions - os.makedirs(LOG_DIR, mode=0o777, exist_ok=True) - print(f"Created/verified log directory at: {LOG_DIR}") - except Exception as e: - print(f"Error creating log directory: {e}", file=sys.stderr) - # Fallback to /tmp if we can't create in the app directory - LOG_DIR = '/tmp/sentiment_logs' - os.makedirs(LOG_DIR, mode=0o777, exist_ok=True) - print(f"Using fallback log directory at: {LOG_DIR}") - - LOG_FILE = os.path.join(LOG_DIR, 'app.log') - - # Create formatters - formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') - - # Create handlers - file_handler = logging.FileHandler(LOG_FILE) - file_handler.setFormatter(formatter) - file_handler.setLevel(logging.INFO) - - console_handler = logging.StreamHandler() - console_handler.setFormatter(formatter) - console_handler.setLevel(logging.INFO) - - # Get the root logger - root_logger = logging.getLogger() - root_logger.setLevel(logging.INFO) - - # Remove any existing handlers - for handler in root_logger.handlers[:]: - root_logger.removeHandler(handler) - - # Add our handlers - root_logger.addHandler(file_handler) - root_logger.addHandler(console_handler) - - # Test logging - logger = logging.getLogger(__name__) - logger.info("Logging system initialized") - return logger \ No newline at end of file diff --git a/apps/backend/main.py b/apps/backend/main.py new file mode 100755 index 0000000..d39f97e --- /dev/null +++ b/apps/backend/main.py @@ -0,0 +1,26 @@ +from fastapi import FastAPI +from fastapi.middleware.cors import CORSMiddleware +from routers import upload, chat + +app = FastAPI(title="Academic RAG API", version="1.0.0") + +# CORS middleware +app.add_middleware( + CORSMiddleware, + allow_origins=["http://localhost:3000"], + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) + +# Include routers +app.include_router(upload.router, prefix="/api", tags=["upload"]) +app.include_router(chat.router, prefix="/api", tags=["chat"]) + +@app.get("/") +async def root(): + return {"message": "Academic RAG API is running"} + +@app.get("/health") +async def health(): + return {"status": "healthy"} diff --git a/apps/backend/rag_engine.py b/apps/backend/rag_engine.py deleted file mode 100644 index 368ca0a..0000000 --- a/apps/backend/rag_engine.py +++ /dev/null @@ -1,424 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- - -""" -RAG Engine Module -Orchestrates the RAG pipeline: chunking, embedding, storage, retrieval, and answer generation -""" - -import os -import logging -import hashlib -from typing import List, Dict, Any, Optional -from datetime import datetime - -from vector_store import VectorStore -from embeddings import get_embedding_generator -from llm_client import get_llm_client - -logger = logging.getLogger(__name__) - - -class RAGEngine: - """Main RAG pipeline orchestrator""" - - def __init__( - self, - vector_store_path: str = "./chroma_db", - embedding_model: str = "BAAI/bge-large-en-v1.5", - llm_api_key: str = None - ): - """ - Initialize RAG engine - - Parameters: - vector_store_path: Path to ChromaDB storage - embedding_model: Name of embedding model to use - llm_api_key: Gemini API key - """ - self.vector_store = VectorStore(persist_directory=vector_store_path) - self.embedding_generator = get_embedding_generator(model_name=embedding_model) - self.llm_client = get_llm_client(api_key=llm_api_key) - - logger.info("RAG Engine initialized successfully") - - def chunk_text( - self, - text: str, - chunk_size: int = 500, - chunk_overlap: int = 50, - section_name: str = None - ) -> List[str]: - """ - Split text into chunks with overlap - - Parameters: - text: Text to chunk - chunk_size: Target size of each chunk (in characters) - chunk_overlap: Overlap between chunks - section_name: Optional section name for context-aware chunking - - Returns: - List of text chunks - """ - if not text or len(text.strip()) == 0: - return [] - - chunks = [] - start = 0 - text_length = len(text) - - while start < text_length: - end = start + chunk_size - - # If we're not at the end, try to break at sentence boundary - if end < text_length: - # Look for sentence endings (., !, ?) within next 100 chars - search_end = min(end + 100, text_length) - sentence_ends = [ - text.rfind('. ', start, search_end), - text.rfind('! ', start, search_end), - text.rfind('? ', start, search_end), - text.rfind('.\n', start, search_end) - ] - - best_end = max([e for e in sentence_ends if e > start], default=end) - if best_end > start: - end = best_end + 1 - - chunk = text[start:end].strip() - - if chunk: - # Add section context if available - if section_name: - chunk = f"[Section: {section_name}]\n{chunk}" - - chunks.append(chunk) - - # Move start position with overlap - start = end - chunk_overlap if end < text_length else text_length - - logger.info(f"Split text into {len(chunks)} chunks") - return chunks - - def ingest_paper( - self, - paper_data: Dict[str, Any], - chunk_size: int = 500, - chunk_overlap: int = 50 - ) -> Dict[str, Any]: - """ - Ingest a paper into the RAG system - - Parameters: - paper_data: Dictionary containing paper information - - paper_id: Unique identifier - - title: Paper title - - authors: Authors string - - year: Publication year - - sections: List of sections with 'name' and 'text' - - abstract: Abstract text - chunk_size: Size of text chunks - chunk_overlap: Overlap between chunks - - Returns: - Dictionary with ingestion results - """ - try: - paper_id = paper_data.get('paper_id') - title = paper_data.get('title', 'Unknown') - authors = paper_data.get('authors', '') - year = paper_data.get('year', '') - sections = paper_data.get('sections', []) - - if not paper_id: - raise ValueError("paper_id is required") - - logger.info(f"Ingesting paper: {title}") - - all_chunks = [] - all_embeddings = [] - all_metadatas = [] - all_ids = [] - - # Process each section - for section in sections: - section_name = section.get('name', 'Unknown') - section_text = section.get('text', '') - page = section.get('page', 'N/A') - - if not section_text.strip(): - continue - - # Chunk section text - chunks = self.chunk_text( - section_text, - chunk_size=chunk_size, - chunk_overlap=chunk_overlap, - section_name=section_name - ) - - for i, chunk in enumerate(chunks): - # Generate unique ID for chunk - chunk_id = self._generate_chunk_id(paper_id, section_name, i) - - all_chunks.append(chunk) - all_ids.append(chunk_id) - all_metadatas.append({ - 'paper_id': paper_id, - 'title': title, - 'authors': authors, - 'year': year, - 'section': section_name, - 'page': str(page), - 'chunk_index': i, - 'upload_date': datetime.now().isoformat() - }) - - if not all_chunks: - return { - 'success': False, - 'error': 'No valid text chunks extracted from paper', - 'chunks_processed': 0 - } - - # Generate embeddings for all chunks - logger.info(f"Generating embeddings for {len(all_chunks)} chunks...") - all_embeddings = self.embedding_generator.encode_text(all_chunks) - - # Store in vector database - success = self.vector_store.add_documents( - documents=all_chunks, - embeddings=all_embeddings, - metadatas=all_metadatas, - ids=all_ids - ) - - if success: - return { - 'success': True, - 'paper_id': paper_id, - 'title': title, - 'chunks_processed': len(all_chunks), - 'sections_processed': len(sections) - } - else: - return { - 'success': False, - 'error': 'Failed to store chunks in vector database', - 'chunks_processed': 0 - } - - except Exception as e: - logger.error(f"Error ingesting paper: {e}") - return { - 'success': False, - 'error': str(e), - 'chunks_processed': 0 - } - - def query( - self, - query: str, - n_results: int = 5, - paper_ids: Optional[List[str]] = None - ) -> Dict[str, Any]: - """ - Query the RAG system and get an answer - - Parameters: - query: User's question - n_results: Number of context chunks to retrieve - paper_ids: Optional list of specific paper IDs to search within - - Returns: - Dictionary with answer, citations, and retrieved contexts - """ - try: - logger.info(f"Processing query: {query}") - - # Generate query embedding - query_embedding = self.embedding_generator.encode_query(query) - - # Build filter if paper_ids specified - where_filter = None - if paper_ids: - # ChromaDB filter syntax - if len(paper_ids) == 1: - where_filter = {"paper_id": paper_ids[0]} - else: - where_filter = {"paper_id": {"$in": paper_ids}} - - # Retrieve relevant chunks - results = self.vector_store.query( - query_embedding=query_embedding, - n_results=n_results, - where=where_filter - ) - - if not results['documents']: - return { - 'answer': 'No relevant information found in the uploaded papers.', - 'citations': [], - 'contexts': [] - } - - # Prepare contexts for LLM - contexts = [] - for doc, metadata, distance in zip( - results['documents'], - results['metadatas'], - results['distances'] - ): - contexts.append({ - 'text': doc, - 'metadata': metadata, - 'relevance_score': 1 - distance # Convert distance to similarity - }) - - # Generate answer using LLM - llm_response = self.llm_client.generate_answer( - query=query, - contexts=contexts - ) - - return { - 'answer': llm_response['answer'], - 'citations': llm_response['citations'], - 'contexts': contexts, - 'model': llm_response.get('model', 'unknown'), - 'contexts_used': len(contexts) - } - - except Exception as e: - logger.error(f"Error processing query: {e}") - return { - 'answer': f'Error processing query: {str(e)}', - 'citations': [], - 'contexts': [], - 'error': str(e) - } - - def compare_papers( - self, - paper_ids: List[str], - comparison_aspects: List[str] = None - ) -> Dict[str, Any]: - """ - Compare multiple papers - - Parameters: - paper_ids: List of paper IDs to compare - comparison_aspects: Specific aspects to compare - - Returns: - Comparison result dictionary - """ - try: - logger.info(f"Comparing {len(paper_ids)} papers") - - papers_data = [] - - for paper_id in paper_ids: - # Get paper chunks - chunks_data = self.vector_store.get_paper_chunks(paper_id) - - if not chunks_data['documents']: - continue - - # Extract title and abstract from metadata - first_metadata = chunks_data['metadatas'][0] if chunks_data['metadatas'] else {} - - # Find abstract in chunks - abstract = "" - for doc, meta in zip(chunks_data['documents'], chunks_data['metadatas']): - if meta.get('section', '').lower() == 'abstract': - abstract = doc - break - - papers_data.append({ - 'paper_id': paper_id, - 'title': first_metadata.get('title', 'Unknown'), - 'authors': first_metadata.get('authors', ''), - 'year': first_metadata.get('year', ''), - 'abstract': abstract or 'No abstract available', - 'sections': len(set(m.get('section') for m in chunks_data['metadatas'])) - }) - - if len(papers_data) < 2: - return { - 'comparison': 'Not enough papers found for comparison', - 'papers_compared': len(papers_data), - 'error': 'Need at least 2 papers' - } - - # Generate comparison using LLM - comparison_result = self.llm_client.generate_comparison( - papers_data=papers_data, - comparison_aspects=comparison_aspects - ) - - comparison_result['papers'] = papers_data - - return comparison_result - - except Exception as e: - logger.error(f"Error comparing papers: {e}") - return { - 'comparison': f'Error comparing papers: {str(e)}', - 'error': str(e) - } - - def list_papers(self) -> List[Dict[str, Any]]: - """ - List all papers in the system - - Returns: - List of papers with metadata - """ - return self.vector_store.list_papers() - - def delete_paper(self, paper_id: str) -> bool: - """ - Delete a paper from the system - - Parameters: - paper_id: Paper ID to delete - - Returns: - True if successful - """ - return self.vector_store.delete_paper(paper_id) - - def get_stats(self) -> Dict[str, Any]: - """ - Get RAG system statistics - - Returns: - Statistics dictionary - """ - papers = self.list_papers() - total_chunks = self.vector_store.count_documents() - - return { - 'total_papers': len(papers), - 'total_chunks': total_chunks, - 'embedding_model': self.embedding_generator.model_name, - 'embedding_dimension': self.embedding_generator.get_embedding_dimension(), - 'llm_model': self.llm_client.model_name - } - - def _generate_chunk_id(self, paper_id: str, section: str, chunk_index: int) -> str: - """ - Generate unique chunk ID - - Parameters: - paper_id: Paper identifier - section: Section name - chunk_index: Index of chunk within section - - Returns: - Unique chunk ID - """ - content = f"{paper_id}_{section}_{chunk_index}" - return hashlib.md5(content.encode()).hexdigest() diff --git a/apps/backend/requirements.txt b/apps/backend/requirements.txt old mode 100644 new mode 100755 index 462d0bf..dad0a83 --- a/apps/backend/requirements.txt +++ b/apps/backend/requirements.txt @@ -1,32 +1,15 @@ -# Flask and web server -Flask==3.0.0 -flask-cors==4.0.0 -Werkzeug==3.0.1 -gunicorn==21.2.0 - -# RAG and Vector Database -chromadb==0.4.22 -sentence-transformers==2.3.1 - -# LLM Integration -google-generativeai==0.3.2 - -# Natural language processing -nltk==3.8.1 -transformers==4.37.2 -torch==2.1.2 -scikit-learn==1.4.0 - -# HTTP and API -requests==2.31.0 - -# Utilities -python-dotenv==1.0.0 -python-dateutil==2.8.2 -tqdm==4.66.1 - -# PDF processing -PyPDF2==3.0.1 - -# XML parsing -lxml==5.1.0 \ No newline at end of file +fastapi>=0.109.0 +uvicorn[standard]>=0.27.0 +python-multipart>=0.0.6 +pydantic>=2.7.0 +langchain>=0.1.0 +langchain-community>=0.0.38 +langchain-text-splitters>=0.0.1 +langchain-google-genai>=0.0.6 +langchain-chroma>=0.1.0 +chromadb>=0.4.22 +PyMuPDF>=1.24.0 +pdfplumber>=0.10.3 +python-dotenv>=1.0.0 +google-generativeai>=0.3.2 +tiktoken>=0.5.2 diff --git a/apps/backend/routers/__init__.py b/apps/backend/routers/__init__.py new file mode 100755 index 0000000..aeb7ccf --- /dev/null +++ b/apps/backend/routers/__init__.py @@ -0,0 +1 @@ +# This file makes the routers directory a Python package diff --git a/apps/backend/routers/chat.py b/apps/backend/routers/chat.py new file mode 100755 index 0000000..6e99e67 --- /dev/null +++ b/apps/backend/routers/chat.py @@ -0,0 +1,30 @@ +from fastapi import APIRouter, HTTPException +from pydantic import BaseModel +from services.rag_service import RAGService + +router = APIRouter() +rag_service = RAGService() + + +class ChatRequest(BaseModel): + keyword: str + document_name: str = None + + +@router.post("/chat") +async def chat(request: ChatRequest): + """Analyze sentiment of research paper against a keyword""" + try: + if not request.keyword: + raise HTTPException(status_code=400, detail="Keyword is required") + + # Perform sentiment analysis + result = rag_service.analyze_sentiment( + keyword=request.keyword, + document_name=request.document_name + ) + + return result + + except Exception as e: + raise HTTPException(status_code=500, detail=f"Error analyzing sentiment: {str(e)}") diff --git a/apps/backend/routers/upload.py b/apps/backend/routers/upload.py new file mode 100755 index 0000000..a24e1d2 --- /dev/null +++ b/apps/backend/routers/upload.py @@ -0,0 +1,52 @@ +from fastapi import APIRouter, UploadFile, File, HTTPException +from services.pdf_processor import PDFProcessor +from services.rag_service import RAGService +from config import settings +import os +import shutil + +router = APIRouter() +pdf_processor = PDFProcessor() +rag_service = RAGService() + + +@router.post("/upload") +async def upload_pdf(file: UploadFile = File(...)): + """Upload and process a PDF document""" + try: + # Validate file type + if not file.filename.endswith('.pdf'): + raise HTTPException(status_code=400, detail="Only PDF files are allowed") + + # Save uploaded file + file_path = os.path.join(settings.UPLOAD_DIR, file.filename) + with open(file_path, "wb") as buffer: + shutil.copyfileobj(file.file, buffer) + + # Process PDF + documents = pdf_processor.extract_text_with_metadata(file_path) + chunks = pdf_processor.chunk_documents(documents) + + # Create/update vector store + rag_service.create_vectorstore(chunks, file.filename) + + return { + "message": "PDF uploaded and processed successfully", + "filename": file.filename, + "pages": len(documents), + "chunks": len(chunks) + } + + except Exception as e: + raise HTTPException(status_code=500, detail=f"Error processing PDF: {str(e)}") + + +@router.get("/documents") +async def list_documents(): + """List all uploaded documents""" + try: + files = os.listdir(settings.UPLOAD_DIR) + pdf_files = [f for f in files if f.endswith('.pdf')] + return {"documents": pdf_files} + except Exception as e: + raise HTTPException(status_code=500, detail=f"Error listing documents: {str(e)}") diff --git a/apps/backend/sentiment_analyzer.py b/apps/backend/sentiment_analyzer.py deleted file mode 100644 index ed9d47f..0000000 --- a/apps/backend/sentiment_analyzer.py +++ /dev/null @@ -1,314 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- - -""" -Sentiment Analysis Module -Using multiple methods to analyze text sentiment -""" - -import logging -import re -import nltk -from nltk.sentiment.vader import SentimentIntensityAnalyzer -from textblob import TextBlob -from collections import Counter - -# Get logger for this module -logger = logging.getLogger(__name__) - -# Ensure NLTK resources are downloaded -try: - nltk.data.find('vader_lexicon') -except LookupError: - logger.info("Downloading NLTK resources...") - nltk.download('vader_lexicon') - nltk.download('punkt') - -class SentimentAnalyzer: - """Sentiment analysis class, providing multiple sentiment analysis methods""" - - def __init__(self): - """Initialize sentiment analyzer""" - self.vader = SentimentIntensityAnalyzer() - - def analyze_text(self, text, method='combined'): - """ - Analyze text sentiment - - Parameters: - text: Text to analyze - method: Analysis method, options: 'vader', 'textblob', 'combined' - - Returns: - Sentiment analysis result dictionary - """ - if not text or text.strip() == "": - logger.warning("Input text is empty or contains only whitespace") - return { - 'sentiment': 'neutral', - 'score': 0, - 'confidence': 0, - 'details': {'message': 'Empty text provided'} - } - - # Clean text - cleaned_text = self._clean_text(text) - - if not cleaned_text or cleaned_text.strip() == "": - logger.warning("Cleaned text is empty") - return { - 'sentiment': 'neutral', - 'score': 0, - 'confidence': 0, - 'details': {'message': 'Text contained only special characters or stopwords'} - } - - # Choose analyzer based on method - if method == 'vader': - return self._analyze_with_vader(cleaned_text) - elif method == 'textblob': - return self._analyze_with_textblob(cleaned_text) - else: # Default to combined method - return self._analyze_combined(cleaned_text) - - def _clean_text(self, text): - """ - Clean text - - Parameters: - text: Original text - - Returns: - Cleaned text - """ - # Remove special characters and extra spaces - text = re.sub(r'[^\w\s.,!?]', ' ', text) - text = re.sub(r'\s+', ' ', text).strip() - return text - - def _analyze_with_vader(self, text): - """ - Use VADER for sentiment analysis - - Parameters: - text: Text to analyze - - Returns: - VADER sentiment analysis result - """ - try: - # Split long text into sentences - sentences = nltk.sent_tokenize(text) - - # Analyze each sentence - sentence_scores = [] - for sentence in sentences: - if sentence: - score = self.vader.polarity_scores(sentence) - sentence_scores.append(score) - - # If no valid sentences, return neutral result - if not sentence_scores: - return { - 'sentiment': 'neutral', - 'score': 0, - 'confidence': 0, - 'details': {'pos': 0, 'neg': 0, 'neu': 1, 'compound': 0} - } - - # Calculate average scores - avg_scores = { - 'pos': sum(s['pos'] for s in sentence_scores) / len(sentence_scores), - 'neg': sum(s['neg'] for s in sentence_scores) / len(sentence_scores), - 'neu': sum(s['neu'] for s in sentence_scores) / len(sentence_scores), - 'compound': sum(s['compound'] for s in sentence_scores) / len(sentence_scores) - } - - # Determine sentiment - compound = avg_scores['compound'] - if compound >= 0.05: - sentiment = 'positive' - elif compound <= -0.05: - sentiment = 'negative' - else: - sentiment = 'neutral' - - # Calculate confidence - confidence = abs(compound) - - return { - 'sentiment': sentiment, - 'score': compound, - 'confidence': confidence, - 'details': avg_scores - } - - except Exception as e: - logger.error(f"VADER analysis error: {e}") - return { - 'sentiment': 'neutral', - 'score': 0, - 'confidence': 0, - 'details': {'error': str(e)} - } - - def _analyze_with_textblob(self, text): - """ - Use TextBlob for sentiment analysis - - Parameters: - text: Text to analyze - - Returns: - TextBlob sentiment analysis result - """ - try: - # Create TextBlob object - blob = TextBlob(text) - - # Get polarity and subjectivity - polarity = blob.sentiment.polarity - subjectivity = blob.sentiment.subjectivity - - # Determine sentiment - if polarity > 0.05: - sentiment = 'positive' - elif polarity < -0.05: - sentiment = 'negative' - else: - sentiment = 'neutral' - - # Calculate confidence - confidence = abs(polarity) * subjectivity - - return { - 'sentiment': sentiment, - 'score': polarity, - 'confidence': confidence, - 'details': { - 'polarity': polarity, - 'subjectivity': subjectivity - } - } - - except Exception as e: - logger.error(f"TextBlob analysis error: {e}") - return { - 'sentiment': 'neutral', - 'score': 0, - 'confidence': 0, - 'details': {'error': str(e)} - } - - def _analyze_combined(self, text): - """ - Use combined method for sentiment analysis - - Parameters: - text: Text to analyze - - Returns: - Combined sentiment analysis result - """ - # Get VADER and TextBlob results - vader_result = self._analyze_with_vader(text) - textblob_result = self._analyze_with_textblob(text) - - # Combine results - sentiments = [vader_result['sentiment'], textblob_result['sentiment']] - sentiment_counter = Counter(sentiments) - - # If both methods agree, use that result - if len(sentiment_counter) == 1: - combined_sentiment = sentiments[0] - else: - # Otherwise, weight by confidence - vader_weight = vader_result['confidence'] - textblob_weight = textblob_result['confidence'] - - # If VADER has higher confidence - if vader_weight > textblob_weight: - combined_sentiment = vader_result['sentiment'] - else: - combined_sentiment = textblob_result['sentiment'] - - # Calculate combined score and confidence - combined_score = (vader_result['score'] + textblob_result['score']) / 2 - combined_confidence = (vader_result['confidence'] + textblob_result['confidence']) / 2 - - return { - 'sentiment': combined_sentiment, - 'score': combined_score, - 'confidence': combined_confidence, - 'details': { - 'vader': vader_result, - 'textblob': textblob_result - } - } - - def analyze_document(self, document_data): - """ - Analyze document sentiment - - Parameters: - document_data: Document data dictionary, including title, abstract, body text, etc. - - Returns: - Document sentiment analysis result - """ - results = {} - - # Analyze title - if document_data.get('title'): - results['title'] = self.analyze_text(document_data['title']) - - # Analyze abstract - if document_data.get('abstract'): - results['abstract'] = self.analyze_text(document_data['abstract']) - - # Analyze body text - if document_data.get('body_text'): - results['body_text'] = self.analyze_text(document_data['body_text']) - - # Analyze full text - if document_data.get('full_text'): - results['full_text'] = self.analyze_text(document_data['full_text']) - else: - # If full text not provided, combine title, abstract, and body text - full_text = ' '.join([ - document_data.get('title', ''), - document_data.get('abstract', ''), - document_data.get('body_text', '') - ]) - results['full_text'] = self.analyze_text(full_text) - - # Determine overall sentiment - if results.get('full_text'): - overall_sentiment = results['full_text']['sentiment'] - overall_score = results['full_text']['score'] - elif results.get('body_text'): - overall_sentiment = results['body_text']['sentiment'] - overall_score = results['body_text']['score'] - elif results.get('abstract'): - overall_sentiment = results['abstract']['sentiment'] - overall_score = results['abstract']['score'] - elif results.get('title'): - overall_sentiment = results['title']['sentiment'] - overall_score = results['title']['score'] - else: - overall_sentiment = 'neutral' - overall_score = 0 - - # Return result - return { - 'overall_sentiment': overall_sentiment, - 'overall_score': overall_score, - 'detailed_results': results - } - -# Test code -if __name__ == "__main__": - analyzer = SentimentAnalyzer() - test_text = "I love this product. It's amazing and works perfectly. However, the customer service was terrible." - result = analyzer.analyze_text(test_text) - print(f"Sentiment: {result['sentiment']}, Score: {result['score']:.2f}, Confidence: {result['confidence']:.2f}") \ No newline at end of file diff --git a/apps/backend/services/__init__.py b/apps/backend/services/__init__.py new file mode 100755 index 0000000..58cc1c2 --- /dev/null +++ b/apps/backend/services/__init__.py @@ -0,0 +1 @@ +# This file makes the services directory a Python package diff --git a/apps/backend/services/pdf_processor.py b/apps/backend/services/pdf_processor.py new file mode 100755 index 0000000..923c866 --- /dev/null +++ b/apps/backend/services/pdf_processor.py @@ -0,0 +1,85 @@ +import fitz # PyMuPDF +import pdfplumber +import re +from typing import List, Dict +from langchain_text_splitters import RecursiveCharacterTextSplitter +from config import settings + + +class PDFProcessor: + def __init__(self): + self.text_splitter = RecursiveCharacterTextSplitter( + chunk_size=settings.CHUNK_SIZE, + chunk_overlap=settings.CHUNK_OVERLAP, + separators=["\n\n", "\n", ". ", " ", ""] + ) + + def extract_text_with_metadata(self, pdf_path: str) -> List[Dict]: + """Extract text from PDF with page numbers and section information""" + documents = [] + + # Use PyMuPDF for text extraction + doc = fitz.open(pdf_path) + + for page_num in range(len(doc)): + page = doc[page_num] + text = page.get_text() + + if text.strip(): + # Try to identify section headers + section = self._identify_section(text) + + documents.append({ + "text": text, + "page_number": page_num + 1, + "section": section + }) + + doc.close() + return documents + + def _identify_section(self, text: str) -> str: + """Simple section header identification""" + # Look for common academic paper sections + lines = text.split('\n') + for line in lines[:5]: # Check first few lines + line_clean = line.strip().upper() + if any(keyword in line_clean for keyword in [ + 'ABSTRACT', 'INTRODUCTION', 'METHODOLOGY', 'METHODS', + 'RESULTS', 'DISCUSSION', 'CONCLUSION', 'REFERENCES' + ]): + return line.strip() + return "Unknown Section" + + def chunk_documents(self, documents: List[Dict]) -> List[Dict]: + """Split documents into chunks while preserving metadata""" + all_chunks = [] + + for doc in documents: + text = doc["text"] + chunks = self.text_splitter.split_text(text) + + for i, chunk in enumerate(chunks): + all_chunks.append({ + "text": chunk, + "page_number": doc["page_number"], + "section": doc["section"], + "chunk_index": i, + "citations": self._extract_citations(chunk) + }) + + return all_chunks + + def _extract_citations(self, text: str) -> List[str]: + """Extract citation patterns from text""" + citations = [] + + # Pattern 1: [1], [2], etc. + numeric_citations = re.findall(r'\[(\d+)\]', text) + citations.extend([f"[{c}]" for c in numeric_citations]) + + # Pattern 2: (Author, Year) + author_year = re.findall(r'\(([A-Z][a-z]+(?:\s+et\s+al\.?)?,\s+\d{4})\)', text) + citations.extend(author_year) + + return list(set(citations)) # Remove duplicates diff --git a/apps/backend/services/rag_service.py b/apps/backend/services/rag_service.py new file mode 100755 index 0000000..2342f29 --- /dev/null +++ b/apps/backend/services/rag_service.py @@ -0,0 +1,163 @@ +from langchain_google_genai import GoogleGenerativeAIEmbeddings, ChatGoogleGenerativeAI +from langchain_chroma import Chroma +from langchain_core.prompts import PromptTemplate +from typing import List, Dict +from config import settings +import os + + +class RAGService: + def __init__(self): + self.embeddings = GoogleGenerativeAIEmbeddings( + model="models/text-embedding-004", + google_api_key=settings.GOOGLE_API_KEY + ) + self.llm = ChatGoogleGenerativeAI( + model="gemini-3-flash-preview", + google_api_key=settings.GOOGLE_API_KEY, + temperature=0.3 + ) + self.vectorstore = None + self.collection_name = "academic_papers" + + def create_vectorstore(self, chunks: List[Dict], document_name: str): + """Create or update vector store with document chunks""" + texts = [chunk["text"] for chunk in chunks] + metadatas = [ + { + "page_number": chunk["page_number"], + "section": chunk["section"], + "chunk_index": chunk["chunk_index"], + "citations": ", ".join(chunk["citations"]), + "document_name": document_name + } + for chunk in chunks + ] + + # Create or update Chroma vectorstore + if self.vectorstore is None: + self.vectorstore = Chroma.from_texts( + texts=texts, + embedding=self.embeddings, + metadatas=metadatas, + collection_name=self.collection_name, + persist_directory=settings.CHROMA_DB_PATH + ) + else: + self.vectorstore.add_texts( + texts=texts, + metadatas=metadatas + ) + + def load_vectorstore(self): + """Load existing vector store""" + if os.path.exists(settings.CHROMA_DB_PATH): + self.vectorstore = Chroma( + collection_name=self.collection_name, + embedding_function=self.embeddings, + persist_directory=settings.CHROMA_DB_PATH + ) + + def analyze_sentiment(self, keyword: str, document_name: str = None) -> Dict: + """Analyze sentiment of paper towards a keyword with citations""" + if self.vectorstore is None: + self.load_vectorstore() + + # Build search query + search_query = f"What is discussed about {keyword}? Include any mentions, opinions, or findings related to {keyword}." + + # Retrieve relevant chunks + if document_name: + results = self.vectorstore.similarity_search( + search_query, + k=settings.TOP_K_RESULTS, + filter={"document_name": document_name} + ) + else: + results = self.vectorstore.similarity_search( + search_query, + k=settings.TOP_K_RESULTS + ) + + if not results: + return { + "sentiment": "No information found", + "summary": f"No relevant information found about '{keyword}' in the document.", + "evidence": [] + } + + # Prepare context with citations + context_parts = [] + evidence = [] + + for doc in results: + metadata = doc.metadata + citation_info = f"(Page {metadata['page_number']}, {metadata['section']})" + context_parts.append(f"{doc.page_content}\n{citation_info}") + + evidence.append({ + "text": doc.page_content[:200] + "..." if len(doc.page_content) > 200 else doc.page_content, + "page_number": metadata['page_number'], + "section": metadata['section'], + "citations": metadata.get('citations', '') + }) + + context = "\n\n---\n\n".join(context_parts) + + # Create prompt for sentiment analysis + prompt = PromptTemplate( + template="""You are analyzing an academic research paper. Based on the following excerpts from the paper, +analyze the sentiment and stance towards the keyword: "{keyword}". + +Context from the paper: +{context} + +Provide a comprehensive analysis with the following structure: +**Overall Sentiment:** [State clearly: Positive, Negative, Neutral, or Mixed] + +Then provide: +1. A summary of how the paper discusses this topic +2. Key claims or findings + +Be specific and reference the page numbers when making claims. + +Analysis:""", + input_variables=["keyword", "context"] + ) + + # Get LLM response + formatted_prompt = prompt.format(keyword=keyword, context=context) + response = self.llm.invoke(formatted_prompt) + + # Parse response - handle both string and list content + if isinstance(response.content, list): + analysis_text = response.content[0].get('text', '') if response.content else '' + else: + analysis_text = response.content + + # Extract sentiment (improved parsing - look for the entire text) + sentiment = "Neutral" + analysis_lower = analysis_text.lower() if isinstance(analysis_text, str) else '' + + # Look for sentiment indicators throughout the text, with priority to earlier mentions + if "sentiment: positive" in analysis_lower or "sentiment:** positive" in analysis_lower: + sentiment = "Positive" + elif "sentiment: negative" in analysis_lower or "sentiment:** negative" in analysis_lower: + sentiment = "Negative" + elif "sentiment: mixed" in analysis_lower or "sentiment:** mixed" in analysis_lower: + sentiment = "Mixed" + elif "sentiment: neutral" in analysis_lower or "sentiment:** neutral" in analysis_lower: + sentiment = "Neutral" + # Fallback: check first 500 chars for keywords + elif "positive" in analysis_lower[:500] and "negative" not in analysis_lower[:500]: + sentiment = "Positive" + elif "negative" in analysis_lower[:500] and "positive" not in analysis_lower[:500]: + sentiment = "Negative" + elif "positive" in analysis_lower[:500] and "negative" in analysis_lower[:500]: + sentiment = "Mixed" + + return { + "sentiment": sentiment, + "summary": analysis_text, + "evidence": evidence + } diff --git a/apps/backend/start_conda.sh b/apps/backend/start_conda.sh deleted file mode 100755 index 7f79f8d..0000000 --- a/apps/backend/start_conda.sh +++ /dev/null @@ -1,72 +0,0 @@ -#!/bin/bash - -# Sentiment Analysis Backend Startup Script (Conda Version) -# This script starts the backend services for the sentiment analysis system using Conda - -# Get the directory where this script is located -SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )" - -# Change to the script directory -cd "$SCRIPT_DIR" - -# Set environment variables -export PORT=5000 -export DEBUG=true -export UPLOAD_FOLDER="./uploads" -export GROBID_URL="http://localhost:8070" - -# Create necessary directories with full permissions -mkdir -p "$UPLOAD_FOLDER" -chmod 777 "$UPLOAD_FOLDER" - -mkdir -p "./logs" -chmod 777 "./logs" - -# Check if conda is installed -if ! command -v conda &> /dev/null; then - echo "Error: Conda is not installed or not in PATH." - echo "Please install Anaconda or Miniconda first." - echo "Visit: https://docs.conda.io/en/latest/miniconda.html" - exit 1 -fi - -# Check if sentiment environment exists, create if not -if ! conda env list | grep -q "sentiment"; then - echo "Creating new conda environment 'sentiment'..." - conda create -y -n sentiment python=3.9 - - # Activate environment and install dependencies - echo "Installing dependencies..." - eval "$(conda shell.bash hook)" - conda activate sentiment - - # Install specific versions of problematic packages first - pip install numpy==1.23.5 - pip install scikit-learn==1.2.2 - pip install nltk==3.8.1 - - # Then install remaining dependencies - pip install -r requirements.txt -else - echo "Using existing conda environment 'sentiment'..." - eval "$(conda shell.bash hook)" - conda activate sentiment -fi - -# Check if GROBID is running -echo "Checking GROBID service..." -curl -s -o /dev/null -w "%{http_code}" $GROBID_URL > /dev/null -if [ $? -ne 0 ]; then - echo "Warning: GROBID service is not available at $GROBID_URL" - echo "Some PDF processing features may not work." - echo "To start GROBID, run the grobid_deployment/deploy-grobid.sh script." -else - echo "GROBID service is running." -fi - -# Start the Flask application -echo "Starting Flask application on port $PORT..." -python app.py - -# Note: This script will keep running until the Flask app is stopped -# To stop the app, press Ctrl+C \ No newline at end of file diff --git a/apps/backend/start_deprecated.sh b/apps/backend/start_deprecated.sh deleted file mode 100755 index a4b0f15..0000000 --- a/apps/backend/start_deprecated.sh +++ /dev/null @@ -1,44 +0,0 @@ -#!/bin/bash - -# Sentiment Analysis Backend Startup Script -# This script starts the backend services for the sentiment analysis system - -# Set environment variables -export PORT=5000 -export DEBUG=true -export UPLOAD_FOLDER="./uploads" -export GROBID_URL="http://localhost:8070" - -# Create necessary directories -mkdir -p $UPLOAD_FOLDER - -# Check if Python virtual environment exists -if [ ! -d "venv" ]; then - echo "Creating Python virtual environment..." - python3 -m venv venv -fi - -# Activate virtual environment -source venv/bin/activate - -# Install dependencies -echo "Installing dependencies..." -pip install -r requirements.txt - -# Check if GROBID is running -echo "Checking GROBID service..." -curl -s -o /dev/null -w "%{http_code}" $GROBID_URL > /dev/null -if [ $? -ne 0 ]; then - echo "Warning: GROBID service is not available at $GROBID_URL" - echo "Some PDF processing features may not work." - echo "To start GROBID, run the deploy-grobid.sh script." -else - echo "GROBID service is running." -fi - -# Start the Flask application -echo "Starting Flask application on port $PORT..." -python app.py - -# Note: This script will keep running until the Flask app is stopped -# To stop the app, press Ctrl+C \ No newline at end of file diff --git a/apps/backend/test/testSample.pdf b/apps/backend/test/testSample.pdf deleted file mode 100644 index b513017..0000000 Binary files a/apps/backend/test/testSample.pdf and /dev/null differ diff --git a/apps/backend/test/test_analyze_topic.py b/apps/backend/test/test_analyze_topic.py deleted file mode 100644 index a001bfc..0000000 --- a/apps/backend/test/test_analyze_topic.py +++ /dev/null @@ -1,376 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- - -""" -Test script for the /analyse endpoint with topic parameter -""" - -import requests -import json -import sys -import os -import unittest -import glob -import time - -# API endpoint -BASE_URL = "http://localhost:5000" - -def get_latest_uploaded_file(): - """ - Get the latest file from the uploads directory - - Returns: - The filename of the latest uploaded file - """ - # Path to uploads directory - upload_dir = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), 'uploads') - - # Check if uploads directory exists - if not os.path.exists(upload_dir): - print(f"Error: Uploads directory not found: {upload_dir}") - return None - - # Get list of files in uploads directory - files = glob.glob(os.path.join(upload_dir, '*')) - - # Check if there are any files - if not files: - print("Error: No files found in uploads directory") - return None - - # Get the latest file based on modification time - latest_file = max(files, key=os.path.getmtime) - - # Get just the filename (not the full path) - filename = os.path.basename(latest_file) - - return filename - -def test_analyse_topic(topic, latest_file=None): - """ - Test analyzing sentiment for a specific topic using the /analyse endpoint - - Parameters: - topic: The topic to analyze - latest_file: Optional filename to analyze - - Returns: - The JSON result of the analysis - """ - print(f"Analyzing sentiment for topic: {topic}") - - # Prepare payload - payload = { - "topic": topic - } - - # Add file_path if provided - if latest_file: - payload["file_path"] = latest_file - print(f"Using file: {latest_file}") - - # Call the analyse endpoint - try: - response = requests.post(f"{BASE_URL}/analyse", json=payload) - response.raise_for_status() # Raise exception for HTTP errors - except requests.exceptions.RequestException as e: - print(f"Error connecting to API: {e}") - return {"status": "error", "error": f"Connection error: {str(e)}"} - - try: - result = response.json() - except json.JSONDecodeError: - print(f"Error decoding JSON response: {response.text}") - return {"status": "error", "error": "Invalid JSON response"} - - print(f"Analysis response status: {result.get('status')}") - - return result - -class TestAnalyseTopic(unittest.TestCase): - """Unit test class for analyse functionality with topics""" - - @classmethod - def setUpClass(cls): - """Set up test class - get the latest uploaded file""" - cls.latest_file = get_latest_uploaded_file() - - # Create test directory and file if it doesn't exist - test_data_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'test_data') - os.makedirs(test_data_dir, exist_ok=True) - - # Get test article path - cls.test_article_path = os.path.join(test_data_dir, 'sample_article.txt') - - # Verify API is accessible before running tests - try: - response = requests.get(f"{BASE_URL}/status") - if response.status_code != 200: - print(f"Warning: API status check failed with code {response.status_code}") - print(f"Response: {response.text}") - else: - print("API status check passed") - except requests.exceptions.RequestException as e: - print(f"Warning: Could not connect to API: {e}") - - def test_analyse_human_beings_topic(self): - """Test analyzing 'human beings' topic sentiment""" - topic = "human beings" - result = test_analyse_topic(topic, self.latest_file) - - self.assertIsNotNone(result) - - # Debug output - print(f"\nFull response for {topic}:") - print(json.dumps(result, indent=2)) - - # Check if the result contains error information - if result.get('status') == 'error': - self.fail(f"API returned error: {result.get('error', 'Unknown error')}") - - self.assertEqual(result.get('status'), 'ok') - self.assertIn('result', result) - - # Print result information - if 'result' in result: - result_data = result['result'] - - # Print topic - print(f"\nTopic: {result_data.get('topic')}") - - # Print sentiment if available - if 'sentiment' in result_data: - sentiment = result_data['sentiment'] - print("\nSentiment:") - - # Check if sentiment has stance information - stance = None - if isinstance(sentiment, dict): - if 'stance' in sentiment: - stance = sentiment.get('stance') - print(f"- Stance: {stance}") - - if 'sentiment' in sentiment: - print(f"- Overall: {sentiment.get('sentiment')} (score: {sentiment.get('score')})") - elif 'label' in sentiment: - print(f"- Overall: {sentiment.get('label')} (score: {sentiment.get('score')})") - else: - print(f"- Overall: {sentiment}") - - # If stance is available, verify it - if stance is not None: - # Use less strict verification - verify stance is positive - positive_stances = ['support', 'positive', 'favor', 'pro'] - stance_match = any(s in stance.lower() for s in positive_stances) - self.assertTrue(stance_match, - f"Expected positive stance for topic '{topic}', got '{stance}'") - print(f"✓ Stance verification passed: '{stance}' for '{topic}' (positive stance)") - else: - print("Note: No stance information available in response") - - # Print keywords if available - if 'keywords' in result_data: - print("\nTop keywords:") - for keyword in result_data['keywords'][:5]: # Print top 5 keywords - print(f"- {keyword['word']} ({keyword['count']})") - - # Print sample text if available - if 'sample_text' in result_data: - print(f"\nSample Text: {result_data['sample_text'][:100]}...") - - def test_analyse_ai_topic(self): - """Test analyzing 'AI' topic sentiment""" - topic = "AI" - result = test_analyse_topic(topic, self.latest_file) - - self.assertIsNotNone(result) - - # Debug output - print(f"\nFull response for {topic}:") - print(json.dumps(result, indent=2)) - - # Check if the result contains error information - if result.get('status') == 'error': - self.fail(f"API returned error: {result.get('error', 'Unknown error')}") - - self.assertEqual(result.get('status'), 'ok') - self.assertIn('result', result) - - # Print result information - if 'result' in result: - result_data = result['result'] - - # Print topic - print(f"\nTopic: {result_data.get('topic')}") - - # Print sentiment if available - if 'sentiment' in result_data: - sentiment = result_data['sentiment'] - print("\nSentiment:") - - # Check if sentiment has stance information - stance = None - if isinstance(sentiment, dict): - if 'stance' in sentiment: - stance = sentiment.get('stance') - print(f"- Stance: {stance}") - - if 'sentiment' in sentiment: - print(f"- Overall: {sentiment.get('sentiment')} (score: {sentiment.get('score')})") - elif 'label' in sentiment: - print(f"- Overall: {sentiment.get('label')} (score: {sentiment.get('score')})") - else: - print(f"- Overall: {sentiment}") - - # If stance is available, verify it - if stance is not None: - # Use less strict verification - check for neutral or similar terms - neutral_stances = ['neutral', 'balanced', 'objective', 'impartial'] - stance_match = any(s in stance.lower() for s in neutral_stances) - self.assertTrue(stance_match, - f"Expected neutral stance for topic '{topic}', got '{stance}'") - print(f"✓ Stance verification passed: '{stance}' for '{topic}' (neutral stance)") - else: - print("Note: No stance information available in response") - - # Print keywords if available - if 'keywords' in result_data: - print("\nTop keywords:") - for keyword in result_data['keywords'][:5]: # Print top 5 keywords - print(f"- {keyword['word']} ({keyword['count']})") - - # Print sample text if available - if 'sample_text' in result_data: - print(f"\nSample Text: {result_data['sample_text'][:100]}...") - - def test_analyse_mpi_topic(self): - """Test analyzing 'mpi' topic (which should not be associated with the document)""" - topic = "mpi" - result = test_analyse_topic(topic, self.latest_file) - - self.assertIsNotNone(result) - - # Debug output - print(f"\nFull response for {topic}:") - print(json.dumps(result, indent=2)) - - # Check for appropriate error status or message - expected_message_parts = [ - "not associated with", - "cannot be analyzed", - "try another" - ] - - # The response format could vary, we'll be more flexible in checking - message_found = False - error_message = "" - - # Function to recursively search in nested dictionaries - def search_in_dict(d, parts): - if not isinstance(d, dict): - return False, "" - - # Check each value in the dictionary - for key, value in d.items(): - if isinstance(value, str): - # Check if all parts are in the string - if all(part.lower() in value.lower() for part in parts): - return True, value - elif isinstance(value, dict): - # Recursively search in nested dictionary - found, msg = search_in_dict(value, parts) - if found: - return True, msg - return False, "" - - # Try to find message in the response - message_found, error_message = search_in_dict(result, expected_message_parts) - - # Also check if it's explicitly marked as an error - # It might be a valid response with different phrasing - error_status = result.get('status') == 'error' - - # Output the result for debugging - print(f"\nResponse for topic '{topic}':") - - # Mark the test as successful if either: - # 1. We found the expected error message parts - # 2. The API explicitly returned an error status - self.assertTrue(message_found or error_status, - f"Expected error response for unrelated topic '{topic}' not found in response") - - if message_found: - print(f"✓ Verification passed: Found error message: '{error_message}'") - elif error_status: - print(f"✓ Verification passed: API returned error status") - else: - print("✗ Verification failed: No error message or status found") - - def test_analyse_with_text_only(self): - """Test analyzing with only text (no topic)""" - # Read test article content - if os.path.exists(self.test_article_path): - with open(self.test_article_path, 'r') as f: - content = f.read() - - # Call analyze endpoint with just text - payload = { - "text": content, - "full_analysis": True - } - - # Use try-except for API call - try: - response = requests.post(f"{BASE_URL}/analyse", json=payload) - response.raise_for_status() - result = response.json() - except requests.exceptions.RequestException as e: - self.fail(f"Error connecting to API: {e}") - return - except json.JSONDecodeError: - self.fail(f"Error decoding JSON response: {response.text}") - return - - # Debug output - print("\nFull response for text-only analysis:") - print(json.dumps(result, indent=2)) - - self.assertIsNotNone(result) - self.assertEqual(result.get('status'), 'ok') - self.assertIn('result', result) - - print("\nText-only analysis (no topic):") - print(f"Status: {result.get('status')}") - - # Print basic result info - if 'result' in result and isinstance(result['result'], dict): - if 'sentiment' in result['result']: - sentiment = result['result']['sentiment'] - print(f"\nOverall sentiment: {sentiment}") - - if 'keywords' in result['result']: - print("\nTop keywords:") - for keyword in result['result']['keywords'][:5]: - print(f"- {keyword['word']} ({keyword['count']})") - else: - self.skipTest(f"Test article not found: {self.test_article_path}") - -if __name__ == "__main__": - if len(sys.argv) > 1: - # If command line argument is provided, use it as topic - topic = sys.argv[1] - latest_file = None - - # If two arguments, second is the file path - if len(sys.argv) > 2: - latest_file = sys.argv[2] - else: - # Try to get latest file - latest_file = get_latest_uploaded_file() - - print(f"Running test with topic: {topic}") - test_analyse_topic(topic, latest_file) - else: - print("Running unit tests") - unittest.main() \ No newline at end of file diff --git a/apps/backend/test/test_pytest.py b/apps/backend/test/test_pytest.py deleted file mode 100644 index 12f6105..0000000 --- a/apps/backend/test/test_pytest.py +++ /dev/null @@ -1,43 +0,0 @@ -import pytest -import requests -import os - -BASE_URL = "http://localhost:5000" - -@pytest.mark.parametrize("file_path", ["tests/sample_paper.pdf"]) -def test_upload_and_analyze_file(file_path): - assert os.path.exists(file_path), f"Test file not found: {file_path}" - - with open(file_path, 'rb') as f: - files = {'file': f} - response = requests.post(f"{BASE_URL}/upload", files=files) - assert response.status_code == 200 - data = response.json() - assert 'result' in data and 'file_info' in data['result'] - saved_filename = data['result']['file_info']['saved_filename'] - - payload = {"file_path": saved_filename} - response = requests.post(f"{BASE_URL}/analyze", json=payload) - assert response.status_code == 200 - result = response.json().get("result", {}) - assert "document" in result - assert "sentiment" in result - -@pytest.mark.parametrize("filename", ["20250317024808_Optimising_DNNs1.pdf"]) -def test_analyze_existing_file(filename): - payload = {"file_path": filename} - response = requests.post(f"{BASE_URL}/analyze", json=payload) - assert response.status_code == 200 - result = response.json().get("result", {}) - assert "document" in result - assert "sentiment" in result - -@pytest.mark.parametrize("topic", ["AI", "climate change", "video games"]) -def test_analyze_topic_only(topic): - payload = {"topic": topic} - response = requests.post(f"{BASE_URL}/analyse", json=payload) - assert response.status_code == 200 - result = response.json().get("result", {}) - assert "topic" in result - assert "sentiment" in result - assert "keywords" in result \ No newline at end of file diff --git a/apps/backend/test/test_upload_file.py b/apps/backend/test/test_upload_file.py deleted file mode 100644 index 8fc3bd7..0000000 --- a/apps/backend/test/test_upload_file.py +++ /dev/null @@ -1,91 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- - -""" -Test script: Test only the file upload functionality -""" - -import requests -import json -import sys -import os -import unittest - -# API endpoint -BASE_URL = "http://localhost:5000" - -def test_upload_file(file_path): - """ - Test uploading a file using the /upload endpoint - - Parameters: - file_path: Path to the file to upload - - Returns: - The JSON result of the upload response - """ - print(f"Uploading file: {file_path}") - - with open(file_path, 'rb') as f: - files = {'file': f} - response = requests.post(f"{BASE_URL}/upload", files=files) - - if response.status_code != 200: - print(f"Error uploading file: {response.status_code}") - print(response.text) - return None - - upload_result = response.json() - print(f"Upload response: {json.dumps(upload_result, indent=2)}") - - if 'result' not in upload_result or 'file_info' not in upload_result['result']: - print("Error: Invalid upload response format") - return None - - # Get the saved filename - saved_filename = upload_result['result']['file_info']['saved_filename'] - print(f"Saved filename: {saved_filename}") - - return upload_result - -class TestUpload(unittest.TestCase): - """Unit test class for upload functionality""" - - def test_valid_file_upload(self): - """Test uploading a valid file""" - # Use the specified test file - test_file = "test/testSample.pdf" - - # Check if the test file exists - if not os.path.exists(test_file): - self.skipTest(f"Test file not found: {test_file}") - - result = test_upload_file(test_file) - self.assertIsNotNone(result) - self.assertIn('result', result) - self.assertIn('file_info', result['result']) - self.assertIn('saved_filename', result['result']['file_info']) - - def test_nonexistent_file(self): - """Test uploading a nonexistent file""" - with self.assertRaises(FileNotFoundError): - test_upload_file("nonexistent_file.txt") - -if __name__ == "__main__": - if len(sys.argv) > 1: - # If command line argument is provided, test that file directly - file_path = sys.argv[1] - if not os.path.exists(file_path): - print(f"Error: File not found: {file_path}") - sys.exit(1) - - test_upload_file(file_path) - else: - # Default to using the specified test file - default_test_file = "test/testSample.pdf" - if os.path.exists(default_test_file): - test_upload_file(default_test_file) - else: - print(f"Error: Default test file not found: {default_test_file}") - print("Running unit tests instead...") - unittest.main() \ No newline at end of file diff --git a/apps/backend/text_processor.py b/apps/backend/text_processor.py deleted file mode 100644 index 5db1792..0000000 --- a/apps/backend/text_processor.py +++ /dev/null @@ -1,777 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- - -""" -Text Processing Module -Provides text extraction, cleaning, and processing functions -""" - -import os -import re -import logging -import json -from pathlib import Path -import nltk -from nltk.corpus import stopwords -from nltk.tokenize import word_tokenize, sent_tokenize -from collections import Counter -from nltk.stem import WordNetLemmatizer -from sklearn.feature_extraction.text import TfidfVectorizer -from sklearn.metrics.pairwise import cosine_similarity - -# Get logger for this module -logger = logging.getLogger(__name__) - -# Download required NLTK data -try: - nltk.data.find('tokenizers/punkt') - nltk.data.find('corpora/stopwords') - nltk.data.find('corpora/wordnet') -except LookupError: - logger.info("Downloading NLTK resources...") - nltk.download('punkt') - nltk.download('stopwords') - nltk.download('wordnet') - -class TextProcessor: - """Text processing class for cleaning and analyzing text""" - - def __init__(self, language='english'): - """ - Initialize text processor - - Parameters: - language: Language for stopwords and processing (default: english) - """ - self.language = language - try: - self.stop_words = set(stopwords.words(language)) - except: - logger.warning(f"Stopwords not available for language: {language}. Using English.") - self.stop_words = set(stopwords.words('english')) - - def clean_text(self, text): - """ - Clean text by removing special characters, extra spaces, etc. - - Parameters: - text: Text to clean - - Returns: - Cleaned text - """ - if not text: - return "" - - # Convert to lowercase - text = text.lower() - - # Remove URLs - text = re.sub(r'https?://\S+|www\.\S+', ' ', text) - - # Remove email addresses - text = re.sub(r'\S+@\S+', ' ', text) - - # Remove special characters but keep punctuation for sentence splitting - text = re.sub(r'[^\w\s.,!?]', ' ', text) - - # Remove extra whitespace - text = re.sub(r'\s+', ' ', text).strip() - - return text - - def remove_stopwords(self, text): - """ - Remove stopwords from text - - Parameters: - text: Text to process - - Returns: - Text with stopwords removed - """ - if not text: - return "" - - words = word_tokenize(text) - filtered_words = [word for word in words if word.lower() not in self.stop_words] - return ' '.join(filtered_words) - - def extract_keywords(self, text, top_n=10): - """ - Extract keywords from text - - Parameters: - text: Input text - top_n: Number of top keywords to return - - Returns: - List of (word, frequency) tuples - """ - # Preprocess text - cleaned_text = self.clean_text(text) - - # Tokenize - words = word_tokenize(cleaned_text) - - # Remove stopwords - stop_words = set(stopwords.words(self.language)) - filtered_words = [word.lower() for word in words if word.isalpha() and word.lower() not in stop_words] - - # Count word frequencies - word_freq = Counter(filtered_words) - - # Return top N keywords - return word_freq.most_common(top_n) - - def extract_sentences(self, text): - """ - Extract sentences from text - - Parameters: - text: Text to process - - Returns: - List of sentences - """ - if not text: - return [] - - # Split text into sentences - sentences = sent_tokenize(text) - - # Clean sentences - cleaned_sentences = [s.strip() for s in sentences if s.strip()] - - return cleaned_sentences - - def summarize_text(self, text, num_sentences=3): - """ - Create a simple extractive summary of text - - Parameters: - text: Text to summarize - num_sentences: Number of sentences to include in summary - - Returns: - Summary text - """ - if not text: - return "" - - # Extract sentences - sentences = self.extract_sentences(text) - - # If text is too short, return original - if len(sentences) <= num_sentences: - return text - - # Calculate word frequency - cleaned_text = self.clean_text(text) - word_freq = Counter(word_tokenize(cleaned_text)) - - # Score sentences based on word frequency - sentence_scores = {} - for i, sentence in enumerate(sentences): - for word in word_tokenize(sentence.lower()): - if word in word_freq: - if i not in sentence_scores: - sentence_scores[i] = 0 - sentence_scores[i] += word_freq[word] - - # Get top N sentences - top_sentence_indices = sorted(sentence_scores, key=sentence_scores.get, reverse=True)[:num_sentences] - top_sentence_indices = sorted(top_sentence_indices) # Sort by position in text - - # Create summary - summary = ' '.join([sentences[i] for i in top_sentence_indices]) - - return summary - - def analyze_text_structure(self, text): - """ - Analyze text structure - - Parameters: - text: Text to analyze - - Returns: - Dictionary with text structure analysis - """ - if not text: - return { - 'sentence_count': 0, - 'word_count': 0, - 'avg_sentence_length': 0, - 'avg_word_length': 0 - } - - # Extract sentences and words - sentences = self.extract_sentences(text) - words = word_tokenize(text) - - # Filter out punctuation - words = [word for word in words if word.isalpha()] - - # Calculate metrics - sentence_count = len(sentences) - word_count = len(words) - - # Average sentence length (in words) - avg_sentence_length = word_count / sentence_count if sentence_count > 0 else 0 - - # Average word length - avg_word_length = sum(len(word) for word in words) / word_count if word_count > 0 else 0 - - return { - 'sentence_count': sentence_count, - 'word_count': word_count, - 'avg_sentence_length': avg_sentence_length, - 'avg_word_length': avg_word_length - } - - def process_text_file(self, file_path): - """ - Process plain text file - - Parameters: - file_path: Path to the text file - - Returns: - Processed text content dictionary - """ - logger.info(f"Processing text file: {file_path}") - - try: - # Check if file exists - if not os.path.exists(file_path): - logger.error(f"File does not exist: {file_path}") - return None - - # Read file content - with open(file_path, 'r', encoding='utf-8', errors='replace') as f: - content = f.read() - - # Use filename as title - title = Path(file_path).stem - - # Split content into paragraphs - paragraphs = [p.strip() for p in content.split('\n\n') if p.strip()] - - # If there are paragraphs, the first one might be an abstract - abstract = paragraphs[0] if paragraphs else "" - - # The rest of the paragraphs as body text - body_text = '\n\n'.join(paragraphs[1:]) if len(paragraphs) > 1 else "" - - # If there's no clear abstract, then the body text is the entire content - if not body_text: - body_text = abstract - abstract = "" - - return { - 'title': title, - 'abstract': abstract, - 'body_text': body_text, - 'full_text': content, - 'references': [] - } - - except Exception as e: - logger.error(f"Error processing text file: {e}") - return None - - def process_json_file(self, file_path): - """ - Process JSON file (such as social media data) - - Parameters: - file_path: Path to the JSON file - - Returns: - Processed text content dictionary - """ - logger.info(f"Processing JSON file: {file_path}") - - try: - # Check if file exists - if not os.path.exists(file_path): - logger.error(f"File does not exist: {file_path}") - return None - - # Read JSON content - with open(file_path, 'r', encoding='utf-8', errors='replace') as f: - data = json.load(f) - - # Process different types of JSON structures - if isinstance(data, list): - # If it's a list, it might be multiple social media posts - return self._process_social_media_posts(data) - elif isinstance(data, dict): - # If it's a dictionary, it might be a single document or specific format - return self._process_json_document(data) - else: - logger.error(f"Unsupported JSON format: {type(data)}") - return None - - except Exception as e: - logger.error(f"Error processing JSON file: {e}") - return None - - def _process_social_media_posts(self, posts): - """ - Process a list of social media posts - - Parameters: - posts: List of posts - - Returns: - Processed text content dictionary - """ - if not posts: - return { - 'title': "Social Media Posts", - 'abstract': "", - 'body_text': "", - 'full_text': "", - 'references': [] - } - - # Extract text content from all posts - post_texts = [] - for post in posts: - # Try to extract text from different fields - text = "" - if isinstance(post, dict): - # Common social media fields - for field in ['text', 'content', 'message', 'caption', 'body', 'tweet']: - if field in post and post[field]: - text = post[field] - break - - # If no text found, try using the entire post - if not text: - text = json.dumps(post, ensure_ascii=False) - elif isinstance(post, str): - text = post - - if text: - post_texts.append(text) - - # Combine all post texts - combined_text = '\n\n'.join(post_texts) - - # Create title - title = f"Social Media Posts ({len(posts)} items)" - - # Use first post as abstract - abstract = post_texts[0] if post_texts else "" - - # Rest of posts as body text - body_text = '\n\n'.join(post_texts[1:]) if len(post_texts) > 1 else "" - - return { - 'title': title, - 'abstract': abstract, - 'body_text': body_text, - 'full_text': combined_text, - 'references': [] - } - - def _process_json_document(self, document): - """ - Process a JSON document - - Parameters: - document: JSON document - - Returns: - Processed text content dictionary - """ - # Try to extract information from common fields - title = "" - abstract = "" - body_text = "" - references = [] - - # Extract title - for field in ['title', 'headline', 'subject', 'name']: - if field in document and document[field]: - title = document[field] - break - - # Extract abstract - for field in ['abstract', 'summary', 'description', 'snippet']: - if field in document and document[field]: - abstract = document[field] - break - - # Extract body text - for field in ['body', 'text', 'content', 'article', 'message']: - if field in document and document[field]: - body_text = document[field] - break - - # Extract references - for field in ['references', 'citations', 'sources']: - if field in document and document[field]: - if isinstance(document[field], list): - references = document[field] - break - - # If no content found, use the entire document - if not (title or abstract or body_text): - full_text = json.dumps(document, ensure_ascii=False) - return { - 'title': "JSON Document", - 'abstract': "", - 'body_text': full_text, - 'full_text': full_text, - 'references': references - } - - # Combine full text - full_text = ' '.join(filter(None, [title, abstract, body_text])) - - return { - 'title': title, - 'abstract': abstract, - 'body_text': body_text, - 'full_text': full_text, - 'references': references - } - - def process_csv_file(self, file_path, text_column=None, title_column=None): - """ - Process CSV file - - Parameters: - file_path: Path to the CSV file - text_column: Text column name or index - title_column: Title column name or index - - Returns: - Processed text content dictionary - """ - logger.info(f"Processing CSV file: {file_path}") - - try: - import pandas as pd - - # Check if file exists - if not os.path.exists(file_path): - logger.error(f"File does not exist: {file_path}") - return None - - # Read CSV file - df = pd.read_csv(file_path) - - # If text column not specified, try to guess - if text_column is None: - # Common text column names - text_columns = ['text', 'content', 'message', 'body', 'description'] - for col in text_columns: - if col in df.columns: - text_column = col - break - - # If still not found, use the longest string column - if text_column is None: - for col in df.columns: - if df[col].dtype == 'object': - text_column = col - break - - # If title column not specified, try to guess - if title_column is None: - # Common title column names - title_columns = ['title', 'headline', 'subject', 'name'] - for col in title_columns: - if col in df.columns: - title_column = col - break - - # Extract text content - if text_column is not None and text_column in df.columns: - texts = df[text_column].dropna().astype(str).tolist() - else: - # If no text column found, use all columns - texts = df.apply(lambda row: ' '.join(row.astype(str)), axis=1).tolist() - - # Extract title - if title_column is not None and title_column in df.columns: - titles = df[title_column].dropna().astype(str).tolist() - title = titles[0] if titles else Path(file_path).stem - else: - title = Path(file_path).stem - - # Combine text - combined_text = '\n\n'.join(texts) - - # Use first row as abstract - abstract = texts[0] if texts else "" - - # Rest of rows as body text - body_text = '\n\n'.join(texts[1:]) if len(texts) > 1 else "" - - return { - 'title': title, - 'abstract': abstract, - 'body_text': body_text, - 'full_text': combined_text, - 'references': [] - } - - except Exception as e: - logger.error(f"Error processing CSV file: {e}") - return None - - def process_file(self, file_path): - """ - Process file, automatically detecting file type - - Parameters: - file_path: Path to the file - - Returns: - Processed text content dictionary - """ - # Get file extension - ext = Path(file_path).suffix.lower() - - # Choose processing method based on extension - if ext in ['.txt', '.md', '.rst']: - return self.process_text_file(file_path) - elif ext in ['.json']: - return self.process_json_file(file_path) - elif ext in ['.csv']: - return self.process_csv_file(file_path) - else: - logger.warning(f"Unsupported file type: {ext}") - # Try processing as text file - return self.process_text_file(file_path) - - def generate_topic_text(self, topic): - """ - Generate text content related to a specific topic - - Parameters: - topic: The topic to generate content for - - Returns: - Text content related to the topic - """ - logger.info(f"Generating text for topic: {topic}") - - # Convert topic to lowercase for case-insensitive matching - topic_lower = topic.lower() - - # Dictionary of predefined topic texts - topic_texts = { - 'ai': """ - Artificial Intelligence (AI) is revolutionizing industries across the globe. - Machine learning algorithms are becoming increasingly sophisticated, enabling computers to perform tasks - that once required human intelligence. Deep learning, a subset of AI, has made significant breakthroughs - in image recognition, natural language processing, and autonomous vehicles. However, concerns about AI ethics, - job displacement, and privacy issues have also emerged. Some experts worry about the potential for AI to - surpass human intelligence, while others see it as a tool that will enhance human capabilities. - Companies like Google, Microsoft, and OpenAI are investing billions in AI research and development. - The future of AI looks promising but requires careful consideration of its societal impacts. - """, - - 'climate change': """ - Climate change represents one of the most pressing challenges of our time. Rising global temperatures - have led to melting ice caps, rising sea levels, and increasingly severe weather events. Many scientists - warn that without immediate action, we face catastrophic consequences. Efforts to combat climate change - include renewable energy adoption, carbon capture technologies, and international agreements like the - Paris Accord. However, progress has been slow, and some countries continue to prioritize economic growth - over environmental concerns. Activists like Greta Thunberg have brought global attention to the urgency - of the situation, while some politicians and industry leaders remain skeptical about the severity of the - problem or the need for dramatic action. - """, - - 'healthcare': """ - The healthcare industry is undergoing rapid transformation due to technological advancements and changing - patient expectations. Telemedicine has expanded access to care, particularly in rural areas and during the - COVID-19 pandemic. Electronic health records have improved information sharing among providers but have - also raised concerns about data security. Precision medicine is enabling more personalized treatment plans - based on genetic factors. However, healthcare costs continue to rise in many countries, creating barriers - to access for vulnerable populations. The debate over universal healthcare remains contentious, with - proponents citing improved outcomes and equity, while critics worry about quality of care and economic impacts. - Mental health services have gained increased attention but still face stigma and funding challenges. - """, - - 'education': """ - Education systems worldwide are evolving to meet the demands of the 21st century. Digital learning tools - and online courses have expanded access to education beyond traditional classrooms. The COVID-19 pandemic - accelerated this trend, forcing schools and universities to adopt remote learning models. However, this - transition highlighted the digital divide, as students without reliable internet access or appropriate - devices fell behind. STEM education has received increased emphasis, reflecting the growing importance of - technical skills in the job market. At the same time, there's recognition of the continued value of liberal - arts education in developing critical thinking and communication skills. Issues of educational equity persist, - with significant disparities in resources and outcomes based on socioeconomic status, race, and geography. - """, - - 'cryptocurrency': """ - Cryptocurrency has emerged as a disruptive force in the financial world. Bitcoin, the first and most - well-known cryptocurrency, has experienced dramatic price fluctuations since its creation in 2009. - Blockchain technology, which underlies most cryptocurrencies, offers potential benefits beyond digital - currencies, including secure supply chain management and voting systems. Some countries have embraced - cryptocurrency, with El Salvador even adopting Bitcoin as legal tender. Others have imposed strict - regulations or outright bans, citing concerns about financial stability, criminal activity, and environmental - impact due to energy-intensive mining operations. The rise of decentralized finance (DeFi) platforms - promises to democratize access to financial services but also introduces new risks and regulatory challenges. - """ - } - - # Check if we have predefined text for this topic - for key, text in topic_texts.items(): - if topic_lower in key or key in topic_lower: - # Clean up the text (remove extra whitespace) - return re.sub(r'\s+', ' ', text).strip() - - # If no predefined text is found, generate a generic response - return f""" - The topic of {topic} encompasses various perspectives and developments. - Some view {topic} positively, citing benefits such as improved efficiency and innovation. - Others express concerns about potential drawbacks and unintended consequences. - Research on {topic} continues to evolve, with new findings regularly emerging. - Experts in the field of {topic} often debate the best approaches and future directions. - Public opinion on {topic} varies widely, influenced by media coverage and personal experiences. - """ - - def extract_topic_sentences(self, text, topic): - """ - Extract sentences that are relevant to the given topic - - Parameters: - text: Text to analyze - topic: Topic to search for - - Returns: - List of topic-relevant sentences - """ - if not text or not topic: - return [] - - # Clean and tokenize topic - topic_words = set(word_tokenize(self.clean_text(topic))) - - # Extract sentences - sentences = self.extract_sentences(text) - - # Score sentences based on topic relevance - topic_sentences = [] - for sentence in sentences: - # Clean and tokenize sentence - sentence_words = set(word_tokenize(self.clean_text(sentence))) - - # Calculate overlap with topic words - overlap = len(sentence_words.intersection(topic_words)) - - # If there's significant overlap, include the sentence - if overlap > 0: - topic_sentences.append(sentence) - - return topic_sentences - - def calculate_topic_relevance(self, text, topic): - """ - Calculate how relevant the text is to the given topic - - Parameters: - text: Text to analyze - topic: Topic to check relevance against - - Returns: - Relevance score between 0 and 1 - """ - if not text or not topic: - return 0.0 - - # Clean and tokenize text and topic - text_words = set(word_tokenize(self.clean_text(text))) - topic_words = set(word_tokenize(self.clean_text(topic))) - - # Calculate word overlap - overlap = len(text_words.intersection(topic_words)) - - # Calculate relevance score - # Normalize by the number of topic words to get a score between 0 and 1 - relevance_score = overlap / len(topic_words) if topic_words else 0 - - return min(1.0, relevance_score) # Cap at 1.0 - - def extract_topic_keywords(self, text, topic, top_n=10): - """ - Extract keywords that are relevant to the given topic - - Parameters: - text: Text to analyze - topic: Topic to focus on - top_n: Number of top keywords to return - - Returns: - List of (word, frequency) tuples for topic-relevant keywords - """ - if not text or not topic: - return [] - - # Clean and tokenize topic - topic_words = set(word_tokenize(self.clean_text(topic))) - - # Extract topic-relevant sentences - topic_sentences = self.extract_topic_sentences(text, topic) - - # If no topic-relevant sentences found, use the whole text - if not topic_sentences: - logger.info(f"No sentences related to topic '{topic}' found for keyword extraction. Using full text.") - # Extract general keywords but prioritize those related to the topic - all_keywords = self.extract_keywords(text, top_n * 2) - - # Filter keywords to prioritize those related to the topic - topic_related = [] - other_keywords = [] - - for word, count in all_keywords: - if word.lower() in topic_words or any(topic_word in word.lower() for topic_word in topic_words): - topic_related.append((word, count)) - else: - other_keywords.append((word, count)) - - # Combine lists, prioritizing topic-related keywords - return topic_related + other_keywords[:max(0, top_n - len(topic_related))] - - # Combine topic-relevant sentences - topic_text = ' '.join(topic_sentences) - - # Extract keywords from topic-relevant text - return self.extract_keywords(topic_text, top_n) - -# Test code -if __name__ == "__main__": - processor = TextProcessor() - test_text = """ - Natural language processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence - concerned with the interactions between computers and human language, in particular how to program computers - to process and analyze large amounts of natural language data. The goal is a computer capable of "understanding" - the contents of documents, including the contextual nuances of the language within them. - """ - - print("Cleaned text:") - cleaned = processor.clean_text(test_text) - print(cleaned) - - print("\nKeywords:") - keywords = processor.extract_keywords(test_text, top_n=5) - for word, count in keywords: - print(f"{word}: {count}") - - print("\nSummary:") - summary = processor.summarize_text(test_text, num_sentences=1) - print(summary) - - print("\nText structure:") - structure = processor.analyze_text_structure(test_text) - for key, value in structure.items(): - print(f"{key}: {value}") \ No newline at end of file diff --git a/apps/backend/vector_store.py b/apps/backend/vector_store.py deleted file mode 100644 index 6020b5d..0000000 --- a/apps/backend/vector_store.py +++ /dev/null @@ -1,227 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- - -""" -Vector Store Module -Manages ChromaDB for storing and retrieving document embeddings -""" - -import os -import logging -import chromadb -from chromadb.config import Settings -from typing import List, Dict, Any, Optional - -logger = logging.getLogger(__name__) - - -class VectorStore: - """Manages vector database operations using ChromaDB""" - - def __init__(self, persist_directory: str = "./chroma_db"): - """ - Initialize ChromaDB vector store - - Parameters: - persist_directory: Directory to persist the vector database - """ - self.persist_directory = persist_directory - os.makedirs(persist_directory, exist_ok=True) - - # Initialize ChromaDB client - self.client = chromadb.PersistentClient( - path=persist_directory, - settings=Settings( - anonymized_telemetry=False, - allow_reset=True - ) - ) - - # Get or create collection - self.collection_name = "academic_papers" - try: - self.collection = self.client.get_collection(name=self.collection_name) - logger.info(f"Loaded existing collection: {self.collection_name}") - except: - self.collection = self.client.create_collection( - name=self.collection_name, - metadata={"description": "Academic paper chunks with embeddings"} - ) - logger.info(f"Created new collection: {self.collection_name}") - - def add_documents( - self, - documents: List[str], - embeddings: List[List[float]], - metadatas: List[Dict[str, Any]], - ids: List[str] - ) -> bool: - """ - Add documents with embeddings to the vector store - - Parameters: - documents: List of document text chunks - embeddings: List of embedding vectors - metadatas: List of metadata dictionaries - ids: List of unique IDs for each chunk - - Returns: - True if successful - """ - try: - self.collection.add( - documents=documents, - embeddings=embeddings, - metadatas=metadatas, - ids=ids - ) - logger.info(f"Added {len(documents)} documents to vector store") - return True - except Exception as e: - logger.error(f"Error adding documents to vector store: {e}") - return False - - def query( - self, - query_embedding: List[float], - n_results: int = 5, - where: Optional[Dict[str, Any]] = None - ) -> Dict[str, Any]: - """ - Query the vector store for similar documents - - Parameters: - query_embedding: Query embedding vector - n_results: Number of results to return - where: Optional metadata filter - - Returns: - Dictionary with query results including documents, metadatas, distances - """ - try: - results = self.collection.query( - query_embeddings=[query_embedding], - n_results=n_results, - where=where - ) - - logger.info(f"Query returned {len(results['documents'][0])} results") - return { - 'documents': results['documents'][0], - 'metadatas': results['metadatas'][0], - 'distances': results['distances'][0], - 'ids': results['ids'][0] - } - except Exception as e: - logger.error(f"Error querying vector store: {e}") - return { - 'documents': [], - 'metadatas': [], - 'distances': [], - 'ids': [] - } - - def get_paper_chunks(self, paper_id: str) -> Dict[str, Any]: - """ - Get all chunks for a specific paper - - Parameters: - paper_id: Paper ID to retrieve chunks for - - Returns: - Dictionary with all chunks and metadata for the paper - """ - try: - results = self.collection.get( - where={"paper_id": paper_id} - ) - logger.info(f"Retrieved {len(results['documents'])} chunks for paper {paper_id}") - return results - except Exception as e: - logger.error(f"Error retrieving paper chunks: {e}") - return {'documents': [], 'metadatas': [], 'ids': []} - - def list_papers(self) -> List[Dict[str, Any]]: - """ - List all papers in the vector store - - Returns: - List of unique papers with their metadata - """ - try: - # Get all documents - all_docs = self.collection.get() - - # Extract unique papers - papers = {} - for metadata in all_docs['metadatas']: - paper_id = metadata.get('paper_id') - if paper_id and paper_id not in papers: - papers[paper_id] = { - 'paper_id': paper_id, - 'title': metadata.get('title', 'Unknown'), - 'authors': metadata.get('authors', ''), - 'year': metadata.get('year', ''), - 'upload_date': metadata.get('upload_date', '') - } - - return list(papers.values()) - except Exception as e: - logger.error(f"Error listing papers: {e}") - return [] - - def delete_paper(self, paper_id: str) -> bool: - """ - Delete all chunks for a specific paper - - Parameters: - paper_id: Paper ID to delete - - Returns: - True if successful - """ - try: - # Get all chunk IDs for this paper - results = self.collection.get(where={"paper_id": paper_id}) - if results['ids']: - self.collection.delete(ids=results['ids']) - logger.info(f"Deleted {len(results['ids'])} chunks for paper {paper_id}") - return True - else: - logger.warning(f"No chunks found for paper {paper_id}") - return False - except Exception as e: - logger.error(f"Error deleting paper: {e}") - return False - - def count_documents(self) -> int: - """ - Count total documents in the vector store - - Returns: - Number of documents - """ - try: - return self.collection.count() - except Exception as e: - logger.error(f"Error counting documents: {e}") - return 0 - - def reset(self) -> bool: - """ - Reset the entire vector store (use with caution!) - - Returns: - True if successful - """ - try: - self.client.delete_collection(name=self.collection_name) - self.collection = self.client.create_collection( - name=self.collection_name, - metadata={"description": "Academic paper chunks with embeddings"} - ) - logger.warning("Vector store has been reset!") - return True - except Exception as e: - logger.error(f"Error resetting vector store: {e}") - return False diff --git a/apps/frontend/.dockerignore b/apps/frontend/.dockerignore new file mode 100644 index 0000000..9d97b8e --- /dev/null +++ b/apps/frontend/.dockerignore @@ -0,0 +1,11 @@ +node_modules/ +.next/ +.git +.gitignore +*.md +.env* +!.env.example +npm-debug.log* +.DS_Store +coverage/ +.nyc_output/ diff --git a/apps/frontend/.gitignore b/apps/frontend/.gitignore old mode 100644 new mode 100755 index 5ef6a52..c1bad9d --- a/apps/frontend/.gitignore +++ b/apps/frontend/.gitignore @@ -1,41 +1,9 @@ -# See https://help.github.com/articles/ignoring-files/ for more about ignoring files. - -# dependencies -/node_modules -/.pnp -.pnp.* -.yarn/* -!.yarn/patches -!.yarn/plugins -!.yarn/releases -!.yarn/versions - -# testing -/coverage - -# next.js -/.next/ -/out/ - -# production -/build - -# misc -.DS_Store -*.pem - -# debug -npm-debug.log* -yarn-debug.log* -yarn-error.log* -.pnpm-debug.log* - -# env files (can opt-in for committing if needed) -.env* - -# vercel -.vercel - -# typescript -*.tsbuildinfo -next-env.d.ts +node_modules/ +.next/ +out/ +.env.local +.env.development.local +.env.test.local +.env.production.local +*.log +.DS_Store diff --git a/apps/frontend/Dockerfile b/apps/frontend/Dockerfile index cc7afd4..808f3a4 100644 --- a/apps/frontend/Dockerfile +++ b/apps/frontend/Dockerfile @@ -1,24 +1,40 @@ -# Frontend Dockerfile for ScholarSense - Simplified Production Build -FROM node:20-alpine +# Build stage +FROM node:20-alpine AS builder WORKDIR /app +# Copy package files +COPY package*.json ./ + # Install dependencies -COPY package.json package-lock.json* ./ -RUN npm install || npm install --legacy-peer-deps +RUN npm ci # Copy source code COPY . . -# Build the app +# Build the application RUN npm run build -# Expose port -EXPOSE 3000 +# Production stage +FROM node:20-alpine AS runner + +WORKDIR /app ENV NODE_ENV=production + +# Create non-root user +RUN addgroup --system --gid 1001 nodejs && \ + adduser --system --uid 1001 nextjs + +# Copy built assets (public folder is optional) +COPY --from=builder /app/.next/standalone ./ +COPY --from=builder /app/.next/static ./.next/static + +USER nextjs + +EXPOSE 3000 + ENV PORT=3000 ENV HOSTNAME="0.0.0.0" -# Start Next.js -CMD ["npm", "run", "start"] +CMD ["node", "server.js"] diff --git a/apps/frontend/Dockerfile.dev b/apps/frontend/Dockerfile.dev new file mode 100644 index 0000000..56c7077 --- /dev/null +++ b/apps/frontend/Dockerfile.dev @@ -0,0 +1,17 @@ +FROM node:20-alpine + +WORKDIR /app + +# Copy package files +COPY package*.json ./ + +# Install dependencies +RUN npm install + +# Copy source code +COPY . . + +EXPOSE 3000 + +# Run in development mode with hot-reload +CMD ["npm", "run", "dev"] diff --git a/apps/frontend/README.md b/apps/frontend/README.md deleted file mode 100644 index 235389c..0000000 --- a/apps/frontend/README.md +++ /dev/null @@ -1,115 +0,0 @@ -# Sentiment Analysis Frontend Service - -## Project Overview -A Next.js-based sentiment analysis frontend that provides a user-friendly interface for text and document sentiment analysis. The application allows users to upload documents (PDF), specify topics for analysis, and view sentiment analysis results with visual indicators. - -## Technology Stack -- **Next.js**: React framework with server-side rendering capabilities -- **React**: JavaScript library for building user interfaces -- **TypeScript**: Typed JavaScript for improved developer experience -- **Tailwind CSS**: Utility-first CSS framework for styling -- **Next API Routes**: API routing within the Next.js application - -## Project Structure -``` -frontend/ -├── src/ -│ └── app/ -│ ├── api/ -│ │ ├── analyse/ -│ │ │ └── route.ts # API route for sentiment analysis -│ │ └── upload/ -│ │ └── route.ts # API route for file upload -│ ├── favicon.ico -│ ├── globals.css # Global CSS styles -│ ├── layout.tsx # Root layout component -│ └── page.tsx # Main page component with UI -├── public/ # Static assets directory -├── package.json # Project dependencies -├── tailwind.config.ts # Tailwind CSS configuration -├── tsconfig.json # TypeScript configuration -└── next.config.ts # Next.js configuration -``` - -## Environment Requirements -- Node.js 18.0 or higher -- npm or yarn package manager -- Connection to the backend service - -## Deployment Steps -1. Install dependencies: -```bash -npm install -# or -yarn install -``` - -2. Start the development server: -```bash -npm run dev -# or -yarn dev -``` - -3. Build for production: -```bash -npm run build -# or -yarn build -``` - -4. Start the production server: -```bash -npm start -# or -yarn start -``` - -## Configuration -- Backend API is configured to connect to `http://localhost:5000` by default -- File upload size and types are validated both client and server-side - -## Frontend Features - -### 1. Document Upload -- Drag-and-drop interface for uploading PDF files -- File validation to ensure only PDFs are accepted -- Visual feedback for upload status - -### 2. Topic-based Analysis -- Input field for specifying analysis topic -- Validation to ensure required information is provided - -### 3. Results Display -- Sentiment analysis results with visual indicators -- Support/Oppose/Neutral stance indication with color coding -- Display of relevance scores and sentiment percentages -- Topic-related sentences and keywords highlighting - -## API Integration - -### 1. File Upload API -- **Endpoint**: `/api/upload` -- **Method**: POST -- **Function**: Forwards file uploads to the backend service - -### 2. Analysis API -- **Endpoint**: `/api/analyse` -- **Method**: POST -- **Function**: Sends analysis requests to the backend with file path and topic - -## Error Handling -The frontend implements the following error handling: -- File type validation (PDF only) -- Required field validation -- API error handling with user-friendly messages -- Network error handling - -## Development Notes -1. Run `npm run dev` for the development environment with hot reloading -2. The development server runs on `http://localhost:3000` by default -3. Backend connection is required for API functionality - -## Browser Compatibility -- Chrome/Edge/Firefox/Safari (latest versions) -- Responsive design for desktop and mobile devices diff --git a/apps/frontend/app/globals.css b/apps/frontend/app/globals.css new file mode 100755 index 0000000..f11f08e --- /dev/null +++ b/apps/frontend/app/globals.css @@ -0,0 +1,5 @@ +@tailwind base; +@tailwind components; +@tailwind utilities; + +@import 'katex/dist/katex.min.css'; diff --git a/apps/frontend/app/layout.tsx b/apps/frontend/app/layout.tsx new file mode 100755 index 0000000..3ba85a9 --- /dev/null +++ b/apps/frontend/app/layout.tsx @@ -0,0 +1,19 @@ +import './globals.css' +import type { Metadata } from 'next' + +export const metadata: Metadata = { + title: 'Academic RAG Assistant', + description: 'Research paper sentiment analysis tool', +} + +export default function RootLayout({ + children, +}: { + children: React.ReactNode +}) { + return ( + + {children} + + ) +} diff --git a/apps/frontend/app/page.tsx b/apps/frontend/app/page.tsx new file mode 100755 index 0000000..7857843 --- /dev/null +++ b/apps/frontend/app/page.tsx @@ -0,0 +1,63 @@ +'use client'; + +import { useState } from 'react'; +import UploadTab from '@/components/UploadTab'; +import ChatTab from '@/components/ChatTab'; + +export default function Home() { + const [activeTab, setActiveTab] = useState<'upload' | 'chat'>('upload'); + const [uploadedDocuments, setUploadedDocuments] = useState([]); + + const handleUploadSuccess = (filename: string) => { + if (!uploadedDocuments.includes(filename)) { + setUploadedDocuments([...uploadedDocuments, filename]); + } + }; + + return ( +
+
+
+ {/* Header */} +
+

Scholar Sense

+

Sentiment analysis for research papers

+
+ + {/* Tab Navigation */} +
+ + +
+ + {/* Tab Content */} +
+ {activeTab === 'upload' ? ( + + ) : ( + + )} +
+
+
+
+ ); +} diff --git a/apps/frontend/components/ChatTab.tsx b/apps/frontend/components/ChatTab.tsx new file mode 100755 index 0000000..16976c4 --- /dev/null +++ b/apps/frontend/components/ChatTab.tsx @@ -0,0 +1,205 @@ +'use client'; + +import { useState } from 'react'; +import axios from 'axios'; +import ReactMarkdown from 'react-markdown'; +import remarkGfm from 'remark-gfm'; +import remarkMath from 'remark-math'; +import rehypeKatex from 'rehype-katex'; + +interface ChatTabProps { + documents: string[]; +} + +interface Evidence { + text: string; + page_number: number; + section: string; + citations: string; +} + +interface AnalysisResult { + sentiment: string; + summary: string; + evidence: Evidence[]; +} + +export default function ChatTab({ documents }: ChatTabProps) { + const [keyword, setKeyword] = useState(''); + const [selectedDocument, setSelectedDocument] = useState(''); + const [analyzing, setAnalyzing] = useState(false); + const [result, setResult] = useState(null); + + const API_URL = process.env.NEXT_PUBLIC_API_URL || 'http://localhost:8000'; + + const handleAnalyze = async () => { + if (!keyword.trim()) { + alert('Please enter a keyword'); + return; + } + + setAnalyzing(true); + setResult(null); + + try { + const response = await axios.post(`${API_URL}/api/chat`, { + keyword: keyword.trim(), + document_name: selectedDocument || null, + }); + + setResult(response.data); + } catch (error: any) { + alert(error.response?.data?.detail || 'Error analyzing sentiment'); + } finally { + setAnalyzing(false); + } + }; + + const getSentimentColor = (sentiment: string) => { + const s = sentiment.toLowerCase(); + if (s.includes('positive')) return 'text-green-600 bg-green-50 border-green-200'; + if (s.includes('negative')) return 'text-red-600 bg-red-50 border-red-200'; + if (s.includes('mixed')) return 'text-orange-600 bg-orange-50 border-orange-200'; + return 'text-gray-600 bg-gray-50 border-gray-200'; + }; + + return ( +
+
+

Sentiment Analysis

+

+ Analyze the research paper's sentiment towards a specific keyword or topic. +

+
+ + {/* Input Section */} +
+
+ + setKeyword(e.target.value)} + placeholder="e.g., machine learning, neural networks, data privacy..." + className="w-full px-4 py-3 border border-gray-300 rounded-lg focus:ring-2 focus:ring-blue-500 focus:border-transparent" + onKeyPress={(e) => { + if (e.key === 'Enter') handleAnalyze(); + }} + /> +
+ + {documents.length > 0 && ( +
+ + +
+ )} + + +
+ + {/* Results Section */} + {result && ( +
+ {/* Sentiment Badge */} +
+ Overall Sentiment: + + {result.sentiment} + +
+ + {/* Summary */} +
+

Analysis Summary

+
+ {/* @ts-ignore */} + + {result.summary} + +
+
+ + {/* Evidence with Citations */} + {result.evidence && result.evidence.length > 0 && ( +
+

+ Supporting Evidence +

+
+ {result.evidence.map((evidence, index) => ( +
+

"{evidence.text}"

+
+ + 📄 Page {evidence.page_number} + + + 📑 {evidence.section} + + {evidence.citations && ( + + 📚 {evidence.citations} + + )} +
+
+ ))} +
+
+ )} +
+ )} + + {/* Empty State */} + {!result && !analyzing && ( +
+
🔍
+

+ Enter a keyword above to analyze the sentiment of your research papers +

+ {documents.length === 0 && ( +

+ ⚠️ No documents uploaded yet. Upload a PDF first! +

+ )} +
+ )} +
+ ); +} diff --git a/apps/frontend/components/UploadTab.tsx b/apps/frontend/components/UploadTab.tsx new file mode 100755 index 0000000..0bc935d --- /dev/null +++ b/apps/frontend/components/UploadTab.tsx @@ -0,0 +1,204 @@ +'use client'; + +import { useState, useCallback } from 'react'; +import axios from 'axios'; + +interface UploadTabProps { + onUploadSuccess: (filename: string) => void; +} + +export default function UploadTab({ onUploadSuccess }: UploadTabProps) { + const [file, setFile] = useState(null); + const [uploading, setUploading] = useState(false); + const [uploadStatus, setUploadStatus] = useState<{ + type: 'success' | 'error' | null; + message: string; + }>({ type: null, message: '' }); + const [dragActive, setDragActive] = useState(false); + + const API_URL = process.env.NEXT_PUBLIC_API_URL || 'http://localhost:8000'; + + const handleDrag = useCallback((e: React.DragEvent) => { + e.preventDefault(); + e.stopPropagation(); + if (e.type === 'dragenter' || e.type === 'dragover') { + setDragActive(true); + } else if (e.type === 'dragleave') { + setDragActive(false); + } + }, []); + + const handleDrop = useCallback((e: React.DragEvent) => { + e.preventDefault(); + e.stopPropagation(); + setDragActive(false); + + if (e.dataTransfer.files && e.dataTransfer.files[0]) { + const droppedFile = e.dataTransfer.files[0]; + if (droppedFile.type === 'application/pdf') { + setFile(droppedFile); + setUploadStatus({ type: null, message: '' }); + } else { + setUploadStatus({ type: 'error', message: 'Please upload a PDF file' }); + } + } + }, []); + + const handleFileChange = (e: React.ChangeEvent) => { + if (e.target.files && e.target.files[0]) { + const selectedFile = e.target.files[0]; + if (selectedFile.type === 'application/pdf') { + setFile(selectedFile); + setUploadStatus({ type: null, message: '' }); + } else { + setUploadStatus({ type: 'error', message: 'Please upload a PDF file' }); + } + } + }; + + const handleUpload = async () => { + if (!file) { + setUploadStatus({ type: 'error', message: 'Please select a file first' }); + return; + } + + setUploading(true); + setUploadStatus({ type: null, message: '' }); + + const formData = new FormData(); + formData.append('file', file); + + try { + const response = await axios.post(`${API_URL}/api/upload`, formData, { + headers: { + 'Content-Type': 'multipart/form-data', + }, + }); + + setUploadStatus({ + type: 'success', + message: `Successfully processed ${response.data.filename} (${response.data.pages} pages, ${response.data.chunks} chunks)`, + }); + onUploadSuccess(response.data.filename); + setFile(null); + } catch (error: any) { + setUploadStatus({ + type: 'error', + message: error.response?.data?.detail || 'Error uploading file', + }); + } finally { + setUploading(false); + } + }; + + return ( +
+
+

Welcome!

+

+ Upload your academic research paper (PDF) to begin sentiment analysis. +

+
+ + {/* Drag and Drop Area */} +
+
+ + + +
+ +
+

+ Drag and drop your PDF here, or +

+ +
+ + {file && ( +
+

Selected file:

+

{file.name}

+

+ {(file.size / 1024 / 1024).toFixed(2)} MB +

+
+ )} +
+ + {/* Upload Button */} + {file && ( +
+ +
+ )} + + {/* Status Messages */} + {uploadStatus.type && ( +
+

+ {uploadStatus.type === 'success' ? '✓ Success!' : '✗ Error'} +

+

{uploadStatus.message}

+
+ )} + + {/* Info Box */} +
+

How it works:

+
    +
  • • Upload your research paper in PDF format
    • +
  • • The system extracts text and identifies sections
    • +
  • • Content is chunked and embedded into a vector database
    • +
  • • Switch to the Chat tab to analyze sentiment against keywords
    • +
+
+
+ ); +} diff --git a/apps/frontend/eslint.config.mjs b/apps/frontend/eslint.config.mjs deleted file mode 100644 index c85fb67..0000000 --- a/apps/frontend/eslint.config.mjs +++ /dev/null @@ -1,16 +0,0 @@ -import { dirname } from "path"; -import { fileURLToPath } from "url"; -import { FlatCompat } from "@eslint/eslintrc"; - -const __filename = fileURLToPath(import.meta.url); -const __dirname = dirname(__filename); - -const compat = new FlatCompat({ - baseDirectory: __dirname, -}); - -const eslintConfig = [ - ...compat.extends("next/core-web-vitals", "next/typescript"), -]; - -export default eslintConfig; diff --git a/apps/frontend/next-env.d.ts b/apps/frontend/next-env.d.ts new file mode 100755 index 0000000..4eb833e --- /dev/null +++ b/apps/frontend/next-env.d.ts @@ -0,0 +1,5 @@ +/// +/// + +// NOTE: This file should not be edited +// see https://nextjs.org/docs/app/building-your-application/configuring/typescript for more information. diff --git a/apps/frontend/next.config.js b/apps/frontend/next.config.js new file mode 100755 index 0000000..f73d6cd --- /dev/null +++ b/apps/frontend/next.config.js @@ -0,0 +1,7 @@ +/** @type {import('next').NextConfig} */ +const nextConfig = { + reactStrictMode: true, + output: 'standalone', +} + +module.exports = nextConfig diff --git a/apps/frontend/next.config.ts b/apps/frontend/next.config.ts deleted file mode 100644 index bc133fd..0000000 --- a/apps/frontend/next.config.ts +++ /dev/null @@ -1,7 +0,0 @@ -import type { NextConfig } from 'next'; - -const nextConfig: NextConfig = { - // Production configuration -}; - -export default nextConfig; \ No newline at end of file diff --git a/package-lock.json b/apps/frontend/package-lock.json similarity index 56% rename from package-lock.json rename to apps/frontend/package-lock.json index 32e73bb..b17379b 100644 --- a/package-lock.json +++ b/apps/frontend/package-lock.json @@ -1,8714 +1,7503 @@ -{ - "name": "sentiment-analysis", - "lockfileVersion": 3, - "requires": true, - "packages": { - "": { - "name": "sentiment-analysis", - "workspaces": [ - "apps/*", - "packages/*" - ], - "devDependencies": { - "prettier": "^3.4.2", - "turbo": "^2.4.0", - "typescript": "5.7.3" - }, - "engines": { - "node": ">=18" - } - }, - "apps/docs": { - "version": "0.1.0", - "extraneous": true, - "dependencies": { - "@repo/ui": "*", - "next": "^15.1.6", - "react": "^19.0.0", - "react-dom": "^19.0.0" - }, - "devDependencies": { - "@repo/eslint-config": "*", - "@repo/typescript-config": "*", - "@types/node": "^22", - 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"dependencies": { - "next": "^15.5.6", - "react": "^19.0.0", - "react-dom": "^19.0.0" - }, - "devDependencies": { - "@eslint/eslintrc": "^3", - "@types/node": "^20", - "@types/react": "^19", - "@types/react-dom": "^19", - "autoprefixer": "^10.4.20", - "eslint": "^9", - "eslint-config-next": "15.1.6", - "postcss": "^8.5.1", - "tailwindcss": "^3.4.17", - "typescript": "^5" - } -} +{ + "name": "rag-frontend", + "version": "0.1.0", + "private": true, + "scripts": { + "dev": "next dev", + "build": "next build", + "start": "next start", + "lint": "next lint" + }, + "dependencies": { + "axios": "^1.6.5", + "katex": "^0.16.27", + "next": "^14.2.35", + "react": "^18.2.0", + "react-dom": "^18.2.0", + "react-markdown": "^9.1.0", + "rehype-katex": "^7.0.1", + "remark-gfm": "^4.0.1", + "remark-math": "^6.0.0" + }, + "devDependencies": { + "@types/node": "^20.10.6", + "@types/react": "^18.2.46", + "@types/react-dom": "^18.2.18", + "autoprefixer": "^10.4.16", + "eslint": "^8.56.0", + "eslint-config-next": "14.0.4", + "postcss": "^8.4.32", + "tailwindcss": "^3.4.0", + "typescript": "^5.3.3" + } +} diff --git a/apps/frontend/postcss.config.js b/apps/frontend/postcss.config.js old mode 100644 new mode 100755 index 33ad091..a03e681 --- a/apps/frontend/postcss.config.js +++ b/apps/frontend/postcss.config.js @@ -1,6 +1,6 @@ -module.exports = { - plugins: { - tailwindcss: {}, - autoprefixer: {}, - }, -} +module.exports = { + plugins: { + tailwindcss: {}, + autoprefixer: {}, + }, +} diff --git a/apps/frontend/postcss.config.mjs b/apps/frontend/postcss.config.mjs deleted file mode 100644 index 1a69fd2..0000000 --- a/apps/frontend/postcss.config.mjs +++ /dev/null @@ -1,8 +0,0 @@ -/** @type {import('postcss-load-config').Config} */ -const config = { - plugins: { - tailwindcss: {}, - }, -}; - -export default config; diff --git a/apps/frontend/public/file.svg b/apps/frontend/public/file.svg deleted file mode 100644 index 004145c..0000000 --- a/apps/frontend/public/file.svg +++ /dev/null @@ -1 +0,0 @@ - \ No newline at end of file diff --git a/apps/frontend/public/globe.svg b/apps/frontend/public/globe.svg deleted file mode 100644 index 567f17b..0000000 --- a/apps/frontend/public/globe.svg +++ /dev/null @@ -1 +0,0 @@ - \ No newline at end of file diff --git a/apps/frontend/public/next.svg b/apps/frontend/public/next.svg deleted file mode 100644 index 5174b28..0000000 --- a/apps/frontend/public/next.svg +++ /dev/null @@ -1 +0,0 @@ - \ No newline at end of file diff --git a/apps/frontend/public/vercel.svg b/apps/frontend/public/vercel.svg deleted file mode 100644 index 7705396..0000000 --- a/apps/frontend/public/vercel.svg +++ /dev/null @@ -1 +0,0 @@ - \ No newline at end of file diff --git a/apps/frontend/public/window.svg b/apps/frontend/public/window.svg deleted file mode 100644 index b2b2a44..0000000 --- a/apps/frontend/public/window.svg +++ /dev/null @@ -1 +0,0 @@ - \ No newline at end of file diff --git a/apps/frontend/src/app/api/analyse/route.ts b/apps/frontend/src/app/api/analyse/route.ts deleted file mode 100644 index 1d18fe2..0000000 --- a/apps/frontend/src/app/api/analyse/route.ts +++ /dev/null @@ -1,18 +0,0 @@ -import { NextResponse } from 'next/server'; - -const API_URL = process.env.NEXT_PUBLIC_API_URL || process.env.API_URL || 'http://backend:5000'; - -export async function POST(request: Request) { - const { topic, file_path } = await request.json(); - - const response = await fetch(`${API_URL}/analyse`, { - method: 'POST', - headers: { - 'Content-Type': 'application/json', - }, - body: JSON.stringify({ topic, file_path }), - }); - - const data = await response.json(); - return NextResponse.json(data, { status: response.status }); -} \ No newline at end of file diff --git a/apps/frontend/src/app/api/upload/route.ts b/apps/frontend/src/app/api/upload/route.ts deleted file mode 100644 index 81ce750..0000000 --- a/apps/frontend/src/app/api/upload/route.ts +++ /dev/null @@ -1,31 +0,0 @@ -import { NextResponse } from 'next/server'; -const API_URL = process.env.NEXT_PUBLIC_API_URL || process.env.API_URL || 'http://backend:5000'; - -export async function POST(request: Request) { - const formData = await request.formData(); - const file = formData.get('file') as File | null; - - if (!file) { - return NextResponse.json({ error: 'No file uploaded' }, { status: 400 }); - } - - if (file.type !== 'application/pdf') { - return NextResponse.json({ error: 'Only PDF files are allowed' }, { status: 400 }); - } - - // Create a new FormData object to send to the backend - const backendFormData = new FormData(); - backendFormData.append('file', file); - - const response = await fetch(`${API_URL}/upload`, { - method: 'POST', - body: backendFormData, - }); - - try { - const data = await response.json(); - return NextResponse.json(data, { status: response.status }); - } catch { - return NextResponse.json({ error: 'Failed to save file' }, { status: 500 }); - } -} \ No newline at end of file diff --git a/apps/frontend/src/app/compare/page.tsx b/apps/frontend/src/app/compare/page.tsx deleted file mode 100644 index 7a0bbef..0000000 --- a/apps/frontend/src/app/compare/page.tsx +++ /dev/null @@ -1,295 +0,0 @@ -'use client'; - -import { useState, useEffect } from 'react'; -import Link from 'next/link'; -import { api } from '@/lib/api'; -import type { Paper, ComparisonResult } from '@/lib/types'; - -export default function ComparePage() { - const [papers, setPapers] = useState([]); - const [selectedPapers, setSelectedPapers] = useState([]); - const [selectedAspects, setSelectedAspects] = useState([ - 'methodology', - 'results', - 'conclusions', - 'limitations', - ]); - const [result, setResult] = useState(null); - const [isLoading, setIsLoading] = useState(false); - const [error, setError] = useState(null); - - const aspects = [ - 'methodology', - 'results', - 'conclusions', - 'limitations', - 'datasets', - 'evaluation', - ]; - - useEffect(() => { - api - .listPapers() - .then(setPapers) - .catch((err) => setError(err.message)); - }, []); - - const togglePaper = (paperId: string) => { - setSelectedPapers((prev) => - prev.includes(paperId) - ? prev.filter((id) => id !== paperId) - : [...prev, paperId] - ); - }; - - const toggleAspect = (aspect: string) => { - setSelectedAspects((prev) => - prev.includes(aspect) - ? prev.filter((a) => a !== aspect) - : [...prev, aspect] - ); - }; - - const handleCompare = async () => { - if (selectedPapers.length < 2) { - setError('Please select at least 2 papers to compare'); - return; - } - - if (selectedAspects.length === 0) { - setError('Please select at least one comparison aspect'); - return; - } - - setIsLoading(true); - setError(null); - setResult(null); - - try { - const comparisonResult = await api.comparePapers( - selectedPapers, - selectedAspects - ); - setResult(comparisonResult); - } catch (err) { - setError(err instanceof Error ? err.message : 'Failed to compare papers'); - } finally { - setIsLoading(false); - } - }; - - return ( -
-
- {/* Header */} -
-

- Compare Papers -

-

- Compare multiple papers across different aspects -

-
- - {/* Selection Panel */} -
- {/* Paper Selection */} -
- - {papers.length > 0 ? ( -
- {papers.map((paper) => ( - - ))} -
- ) : ( -

- No papers available. Upload some papers first. -

- )} -

- {selectedPapers.length} paper(s) selected -

-
- - {/* Aspect Selection */} -
- -
- {aspects.map((aspect) => ( - - ))} -
-
- - {/* Compare Button */} - -
- - {/* Error Message */} - {error && ( -
- Error: {error} -
- )} - - {/* Comparison Results */} - {result && ( -
- {/* Papers Being Compared */} -
-

- Papers Compared ({result.papers_compared}) -

-
- {result.papers.map((paper, idx) => ( -
-
- - {idx + 1} - -
-

{paper.title}

- {paper.authors && ( -

{paper.authors}

- )} - {paper.year && ( -

Year: {paper.year}

- )} -
-
-
- ))} -
-
- - {/* Comparison Analysis */} -
-
-
⚖️
-

- Comparative Analysis -

-
-
- {result.comparison} -
-
-
- 🤖 Model: {result.model} - 📋 Aspects: {result.aspects.join(', ')} -
-
-
-
- )} - - {/* Empty State */} - {papers.length === 0 && ( -
-
📚
-

- No Papers Available -

-

- Upload at least 2 papers to compare them -

- - Upload Papers - -
- )} -
-
- ); -} diff --git a/apps/frontend/src/app/favicon.ico b/apps/frontend/src/app/favicon.ico deleted file mode 100644 index 718d6fe..0000000 Binary files a/apps/frontend/src/app/favicon.ico and /dev/null differ diff --git a/apps/frontend/src/app/globals.css b/apps/frontend/src/app/globals.css deleted file mode 100644 index 6b717ad..0000000 --- a/apps/frontend/src/app/globals.css +++ /dev/null @@ -1,21 +0,0 @@ -@tailwind base; -@tailwind components; -@tailwind utilities; - -:root { - --background: #ffffff; - --foreground: #171717; -} - -@media (prefers-color-scheme: dark) { - :root { - --background: #0a0a0a; - --foreground: #ededed; - } -} - -body { - color: var(--foreground); - background: var(--background); - font-family: Arial, Helvetica, sans-serif; -} diff --git a/apps/frontend/src/app/layout.tsx b/apps/frontend/src/app/layout.tsx deleted file mode 100644 index 7f858de..0000000 --- a/apps/frontend/src/app/layout.tsx +++ /dev/null @@ -1,39 +0,0 @@ -import type { Metadata } from "next"; -import { Geist, Geist_Mono } from "next/font/google"; -import "./globals.css"; - -const geistSans = Geist({ - variable: "--font-geist-sans", - subsets: ["latin"], -}); - -const geistMono = Geist_Mono({ - variable: "--font-geist-mono", - subsets: ["latin"], -}); - -export const metadata: Metadata = { - title: "ScholarSense - Academic RAG Assistant", - description: "Query academic papers with AI-powered citation-grounded answers", -}; - -import { Navbar } from "@/components/navbar"; - -export default function RootLayout({ - children, -}: Readonly<{ - children: React.ReactNode; -}>) { - return ( - - - -
- {children} -
- - - ); -} diff --git a/apps/frontend/src/app/page.tsx b/apps/frontend/src/app/page.tsx deleted file mode 100644 index 9ca22d4..0000000 --- a/apps/frontend/src/app/page.tsx +++ /dev/null @@ -1,133 +0,0 @@ -'use client'; - -import { useState } from 'react'; -import { UploadZone } from '@/components/upload-zone'; -import type { UploadResult } from '@/lib/types'; -import Link from 'next/link'; - -export default function Home() { - const [uploadedPapers, setUploadedPapers] = useState([]); - const [error, setError] = useState(null); - const [success, setSuccess] = useState(null); - - const handleUploadSuccess = (result: UploadResult) => { - setUploadedPapers((prev) => [result, ...prev]); - setSuccess(`Successfully uploaded "${result.title}"`); - setError(null); - - // Clear success message after 5 seconds - setTimeout(() => setSuccess(null), 5000); - }; - - const handleUploadError = (errorMessage: string) => { - setError(errorMessage); - setSuccess(null); - }; - - return ( -
-
- {/* Header */} -
-

- Upload Academic Papers -

-

- Upload PDF papers to query them with AI-powered citation-grounded answers -

-
- - {/* Messages */} - {error && ( -
- Error: {error} -
- )} - - {success && ( -
- Success! {success} -
- )} - - {/* Upload Zone */} - - - {/* Recently Uploaded Papers */} - {uploadedPapers.length > 0 && ( -
-

- Recently Uploaded Papers -

-
- {uploadedPapers.map((paper) => ( -
-
-
-

- {paper.title} -

-
- 📄 {paper.sections_processed} sections - 🔢 {paper.chunks_processed} chunks - 📅 {new Date(paper.upload_date).toLocaleDateString()} -
-
- - Query This Paper - -
-
- ))} -
-
- )} - - {/* Quick Actions */} -
- -
🔍
-

Query Papers

-

- Ask questions across all uploaded papers -

- - - -
📚
-

Paper Library

-

- Browse and manage uploaded papers -

- - - -
⚖️
-

Compare Papers

-

- Compare methodologies and findings -

- -
-
-
- ); -} \ No newline at end of file diff --git a/apps/frontend/src/app/papers/page.tsx b/apps/frontend/src/app/papers/page.tsx deleted file mode 100644 index 99831a7..0000000 --- a/apps/frontend/src/app/papers/page.tsx +++ /dev/null @@ -1,183 +0,0 @@ -'use client'; - -import { useState, useEffect } from 'react'; -import { api } from '@/lib/api'; -import type { Paper } from '@/lib/types'; -import Link from 'next/link'; - -export default function PapersPage() { - const [papers, setPapers] = useState([]); - const [isLoading, setIsLoading] = useState(true); - const [error, setError] = useState(null); - const [deletingId, setDeletingId] = useState(null); - - useEffect(() => { - loadPapers(); - }, []); - - const loadPapers = async () => { - try { - setIsLoading(true); - const papersList = await api.listPapers(); - setPapers(papersList); - setError(null); - } catch (err) { - setError(err instanceof Error ? err.message : 'Failed to load papers'); - } finally { - setIsLoading(false); - } - }; - - const handleDelete = async (paperId: string, title: string) => { - if (!confirm(`Are you sure you want to delete "${title}"?`)) { - return; - } - - setDeletingId(paperId); - try { - await api.deletePaper(paperId); - setPapers((prev) => prev.filter((p) => p.paper_id !== paperId)); - } catch (err) { - alert(err instanceof Error ? err.message : 'Failed to delete paper'); - } finally { - setDeletingId(null); - } - }; - - if (isLoading) { - return ( -
-
-
- - - - -

Loading papers...

-
-
-
- ); - } - - return ( -
-
- {/* Header */} -
-
-

Paper Library

-

- Manage your uploaded academic papers -

-
- - + Upload New Paper - -
- - {/* Error Message */} - {error && ( -
- Error: {error} -
- )} - - {/* Papers Grid */} - {papers.length > 0 ? ( -
- {papers.map((paper) => ( -
-
-
-

- {paper.title} -

- {paper.authors && ( -

- 👤 {paper.authors} -

- )} -
- {paper.year && 📅 {paper.year}} - - 🕒 {new Date(paper.upload_date).toLocaleDateString()} - -
-
-
- - {/* Actions */} -
- - Query - - -
-
- ))} -
- ) : ( -
-
📚
-

- No Papers Yet -

-

- Start by uploading some academic papers to build your library -

- - Upload Your First Paper - -
- )} - - {/* Stats */} - {papers.length > 0 && ( -
-

Library Stats

-
- 📚 {papers.length} paper(s) - - 📅 Latest:{' '} - {new Date( - Math.max(...papers.map((p) => new Date(p.upload_date).getTime())) - ).toLocaleDateString()} - -
-
- )} -
-
- ); -} diff --git a/apps/frontend/src/app/query/page.tsx b/apps/frontend/src/app/query/page.tsx deleted file mode 100644 index a72777e..0000000 --- a/apps/frontend/src/app/query/page.tsx +++ /dev/null @@ -1,288 +0,0 @@ -'use client'; - -import { useState, useEffect, Suspense } from 'react'; -import { useSearchParams } from 'next/navigation'; -import Link from 'next/link'; -import { api } from '@/lib/api'; -import type { QueryResult, Paper } from '@/lib/types'; - -function QueryPageContent() { - const searchParams = useSearchParams(); - const [query, setQuery] = useState(''); - const [papers, setPapers] = useState([]); - const [selectedPapers, setSelectedPapers] = useState([]); - const [result, setResult] = useState(null); - const [isLoading, setIsLoading] = useState(false); - const [error, setError] = useState(null); - - useEffect(() => { - // Load papers - api.listPapers() - .then(setPapers) - .catch((err) => setError(err.message)); - - // Check if paper_id in URL - const paperId = searchParams.get('paper_id'); - if (paperId) { - setSelectedPapers([paperId]); - } - }, [searchParams]); - - const handleSubmit = async (e: React.FormEvent) => { - e.preventDefault(); - - if (!query.trim()) { - setError('Please enter a question'); - return; - } - - setIsLoading(true); - setError(null); - setResult(null); - - try { - const queryResult = await api.queryPapers( - query, - selectedPapers.length > 0 ? selectedPapers : undefined, - 5 - ); - setResult(queryResult); - } catch (err) { - setError(err instanceof Error ? err.message : 'Failed to query papers'); - } finally { - setIsLoading(false); - } - }; - - const togglePaper = (paperId: string) => { - setSelectedPapers((prev) => - prev.includes(paperId) - ? prev.filter((id) => id !== paperId) - : [...prev, paperId] - ); - }; - - return ( -
-
- {/* Header */} -
-

Query Papers

-

- Ask questions and get citation-grounded answers from your papers -

-
- - {/* Query Form */} -
-
- -