Fact-Check Agent Web App

A full-stack, AI-powered web application that automatically extracts claims from uploaded PDFs and fact-checks them against live web sources. Designed specifically to handle "Trap Documents" containing fabricated claims or outdated statistics.

Features

• PDF Parsing: High-fidelity text extraction using PyMuPDF.
• Claim Extraction: Gemini-powered extraction of quantitative claims, dates, and statistics into structured JSON.
• Live Verification: Uses Tavily API to search the live web for evidence supporting or refuting each claim.
• Advanced Evaluation Engine: Differentiates between VERIFIED, INACCURATE (outdated/partially true), and FALSE (hallucinated) claims.
• Beautiful UI: Built with Streamlit, featuring real-time status updates, metric cards, and donut charts.

Project Structure

fact_check_agent/
├── backend/
│   ├── api/            # API endpoints
│   ├── models/         # Pydantic schemas & SQLAlchemy models
│   ├── services/       # Core logic (PDF extraction, LLM, verification)
│   ├── database.py     # Database configuration
│   └── main.py         # FastAPI application entry point
├── frontend/
│   ├── components/     # UI components
│   ├── utils/          # API communication utils
│   └── app.py          # Main Streamlit application
├── tests/              # Generation scripts for trap doc & presentation
├── requirements.txt    # Python dependencies
├── .env.example        # Environment variables template
└── README.md

Setup Instructions

1. Clone the repository and navigate into it.
2. Create a virtual environment and install dependencies:

   python -m venv venv
   source venv/bin/activate  # On Windows: .\venv\Scripts\activate
   pip install -r requirements.txt

3. Set up your .env file by copying .env.example:

   cp .env.example .env

   Add your GEMINI_API_KEY and TAVILY_API_KEY.

Running Locally

1. Start the FastAPI Backend:

   uvicorn backend.main:app --reload --port 8000

2. Start the Streamlit Frontend: Open a new terminal and run:

   streamlit run frontend/app.py

3. Open your browser to the URL provided by Streamlit (usually http://localhost:8501).

Testing with Trap Documents

Run the script to generate a test PDF containing intentionally false and outdated claims:

python tests/generate_trap_doc.py

Upload the generated trap_document.pdf to the Streamlit app to see the verification engine in action.

Generate Presentation

To generate the architecture slide deck:

python tests/generate_presentation.py