Veridata verifies advertisement claims using Google's Gemini API and performs sentiment analysis to help users identify potentially misleading or false advertising statements.

Demo: https://www.youtube.com/watch?v=m1WHTsHwf1E

• AI-Powered Fact Checking: Uses Google Gemini API to analyze and verify advertisement claims
• Sentiment Analysis: Provides detailed sentiment analysis including polarity, subjectivity, and objectivity
• Text & File Input: Support for both direct text input and file uploads
• Credibility Scoring: Assigns credibility scores from 0-100% based on AI analysis
• Detailed Analysis: Provides comprehensive breakdown of claims and potential issues
• Recommendations: Offers suggestions for improving transparency and accuracy

• FastAPI: Modern, fast web framework for building APIs
• Google Gemini AI: Advanced language model for fact-checking and analysis
• TextBlob: Natural language processing library for sentiment analysis
• Python Magic: File type detection and content extraction
• Pydantic: Data validation and settings management

• HTML5: Semantic markup with modern web standards
• CSS3: Modern styling with animations and responsive design
• JavaScript: Interactive functionality and API integration
• Font Awesome: Beautiful icons and UI elements
• Google Fonts: Professional typography

• Python 3.8 or higher
• pip package manager
• Modern web browser

1. Clone or download the project

   cd /path/to/your/project/directory

2. Install Python dependencies

   pip install -r requirements.txt

3. Set up environment variables

   Create a .env file in the project root directory and add your Gemini API key:

   cp .env.example .env

   Then edit the .env file and replace your_gemini_api_key_here with your actual Google Gemini API key:

   GEMINI_API_KEY=your_actual_api_key_here
   

   To get a Gemini API key:

   • Visit Google AI Studio
   • Sign in with your Google account
   • Go to "API Keys" section
   • Create a new API key
   • Copy the key to your .env file

4. Set up the directory structure

   Veridata/
   ├── backend/
   │   └── main.py
   ├── frontend/
   │   ├── index.html
   │   ├── styles.css
   │   └── script.js
   ├── .env                 # Your API key (not tracked in git)
   ├── .env.example         # Template for API key setup
   ├── .gitignore          # Excludes .env from version control
   ├── requirements.txt
   └── README.md
   

1. Navigate to the project directory

2. Run the FastAPI server:

   python backend/main.py

   Or alternatively:

   uvicorn backend.main:app --reload --host 0.0.0.0 --port 8000

3. The backend will be available at http://localhost:8000

4. API documentation will be available at http://localhost:8000/docs

1. Open your web browser

2. Navigate to the frontend directory

3. Open index.html in your browser, or serve it using a local server:

   # Using Python's built-in server
   cd frontend
   python -m http.server 3000

   Then visit http://localhost:3000

1. Click on "Text Input" tab
2. Paste your advertisement text or claim into the textarea
3. Click "Verify Claim" button
4. Wait for the AI analysis to complete
5. Review the detailed results

1. Click on "File Upload" tab
2. Either drag and drop a file or click to browse
3. Select a text file containing advertisement content
4. Click "Verify File" button
5. Wait for processing and review results

Verification Status:

• Verified: Claims appear to be factually accurate
• Partially Verified: Some claims may need additional verification
• Unverified: Claims lack sufficient supporting evidence
• Misleading: Claims appear to be false or misleading

Credibility Score:

• 80-100%: High credibility - Well-supported claims
• 60-79%: Moderate credibility - Some verification needed
• 40-59%: Low credibility - Several questionable claims
• 0-39%: Very low credibility - Mostly misleading content

Sentiment Analysis:

• Polarity: Emotional tone (-1 to +1, negative to positive)
• Subjectivity: Opinion vs. fact (0 to 1, objective to subjective)
• Objectivity: Factual vs. opinion-based content
• Confidence: How certain the sentiment analysis is

• Returns welcome message

• Verifies text content
• Body: {"text": "your advertisement text"}
• Returns: Verification results with credibility score and analysis

• Verifies uploaded file content
• Body: Form data with file upload
• Returns: Same verification results as text endpoint

• Returns API health status

• Edit frontend/styles.css to change colors, fonts, or layout
• Modify frontend/script.js to add new features or change behavior
• Update frontend/index.html to add new sections or modify structure

• Update backend/main.py to modify API endpoints or add new features
• Adjust the Gemini API prompt to change analysis focus
• Add new verification models or data sources

• API Key Security: The Gemini API key is stored in a .env file that is excluded from version control
• Important: Never commit your .env file to version control
• Production Setup: For production deployment, use proper environment variable configuration
• Additional Security: Consider adding rate limiting and user authentication for production use
• Input Validation: All user inputs are validated and sanitized

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Test thoroughly
5. Submit a pull request

This project is open source and available under the MIT License.

Backend won't start:

• Ensure all dependencies are installed: pip install -r requirements.txt
• Check Python version compatibility (3.8+)
• Verify port 8000 is not in use by another application

Frontend can't connect to backend:

• Ensure backend is running on http://localhost:8000
• Check browser console for CORS errors
• Verify API endpoints are accessible

Gemini API errors:

• Check API key validity
• Verify internet connection
• Check API usage limits and quotas

File upload issues:

• Ensure files are text-based or readable
• Check file size limits
• Verify file permissions

• Check the FastAPI documentation at http://localhost:8000/docs
• Review browser console for JavaScript errors
• Check backend logs for Python errors

• Support for more file formats (PDF, Word documents)
• Batch processing for multiple advertisements
• Historical analysis and reporting
• User accounts and saved analyses
• API rate limiting and caching
• Multi-language support