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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 Designed specifically to handle "Trap Documents" containing fabricated claims or outdated statistics.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  • Advanced Evaluation Engine: Differentiates between VERIFIED, INACCURATE (outdated/partially true), and FALSE (hallucinated) claims.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Run the script to generate a test PDF containing intentionally false and outdated claims: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