| --- |
| title: TruthCheck AI |
| emoji: 🛡️ |
| colorFrom: blue |
| colorTo: purple |
| sdk: docker |
| pinned: false |
| --- |
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| # TruthCheck: AI-Powered Fact Verification System |
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| A state-of-the-art **Automated Fact-Checking System** that uses a multi-stage neural pipeline to verify text claims in real-time. It combines **Web Scraping**, **Semantic Search**, and **Natural Language Inference (NLI)** to determine the truthfulness of statements with high precision. |
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| ## 🌟 Key Features |
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| ### 🧠 Advanced AI Core |
| - **Multi-Model Consensus**: Aggregates judgments from `RoBERTa-large-MNLI` and `DeBERTa-v3-large` for robust accuracy. |
| - **Semantic Filtering**: Uses `Sentence-Transformers` to ensure only relevant evidence is analyzed. |
| - **Credibility Weighting**: Automatically assigns higher trust scores to `.gov`, `.edu`, and scientific domains. |
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| ### 💻 Modern "Cyber-Noir" Interface |
| - **Futuristic UI**: deep space blue theme with neon cyan/purple accents using **Tailwind CSS**. |
| - **Real-Time Dashboard**: Track system stats, truth rates, and scan history in the Command Center. |
| - **Interactive Visuals**: Animated confidence gauges, evidence streams, and live "scanning" effects. |
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| ### ⚙️ Enterprise-Ready |
| - **REST API**: Fully documented endpoint (`/api/verify`) for external integration. |
| - **Persistence**: Built-in SQLite database stores all verification history. |
| - **Scalable Architecture**: Modular design separating Extraction, Retrieval, and Classification layers. |
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| ## 🏛️ System Architecture (Top-to-Bottom) |
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| The application follows a strictly layered pipeline architecture: |
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| 1. **Input Layer**: |
| - User submits a claim via the **Web UI** or **API**. |
| - The `ClaimExtractor` identifies factual statements using **spaCy**. |
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| 2. **Retrieval Layer**: |
| - `KeywordExtractor` pulls search terms (Entities/Nouns). |
| - `EvidenceRetriever` scrapes trusted sources (Wikipedia, Google, DuckDuckGo). |
| - Evidence is filtered by domain credibility and semantic similarity. |
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| 3. **Inference Layer (The "Brain")**: |
| - Filtered evidence is paired with the claim (Premise + Hypothesis). |
| - **NLI Models** classify each pair as `Entailment`, `Contradiction`, or `Neutral`. |
| - A weighted voting algorithm calculates the final **Verdict** and **Confidence Score**. |
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| 4. **Presentation Layer**: |
| - Results are returned to the user with a color-coded verdict (Green/Red/Amber). |
| - Data is archived in the `history.db` SQLite database. |
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| ## 🚀 Installation & Setup Guide |
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| Follow these steps to deploy the system locally. |
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| ### Prerequisites |
| - **Python 3.10+** installed. |
| - **Git** installed. |
| - Internet connection (for downloading models). |
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| ### Step 1: Clone the Repository |
| ```bash |
| git clone https://github.com/CHRISDANIEL145/truth-check.git |
| cd truth-check |
| ``` |
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| ### Step 2: Create Virtual Environment |
| Isolate dependencies to avoid conflicts. |
| ```bash |
| # Windows |
| python -m venv venv |
| .\venv\Scripts\activate |
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| # Linux/Mac |
| python3 -m venv venv |
| source venv/bin/activate |
| ``` |
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| ### Step 3: Install Dependencies |
| This will install PyTorch, Transformers, spaCy, and Flask. |
| ```bash |
| pip install -r requirements.txt |
| ``` |
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| ### Step 4: Download Language Models |
| Pre-download the necessary NLI and spaCy models. |
| ```bash |
| python -m spacy download en_core_web_sm |
| ``` |
| *Note: The Transformer models (RoBERTa/DeBERTa) will automatically download on the first run (approx. 3GB).* |
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| ### Step 5: Run the Application |
| Start the Flask server. |
| ```bash |
| python run.py |
| ``` |
| You should see output indicating the server is running on `http://127.0.0.1:5000`. |
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| --- |
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| ## 📖 Usage Guide |
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| ### 1. Using the Analyzer |
| - Navigate to `http://127.0.0.1:5000`. |
| - Type a factual claim (e.g., *"The Great Wall of China is visible from space"*). |
| - Click **INIT_SCAN**. |
| - View the Verdict, Confidence Score, and supporting/contradicting Evidence. |
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| ### 2. The Dashboard |
| - Click **Dashboard** in the top navigation. |
| - View global statistics (Truth Rate, Total Scans). |
| - Review your complete verification history. |
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| ### 3. API Integration |
| Invoke the verification engine programmatically: |
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| **Endpoint:** `POST /api/verify` |
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| **Request:** |
| ```json |
| { |
| "claim": "Water boils at 100 degrees Celsius." |
| } |
| ``` |
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| **Response:** |
| ```json |
| { |
| "label": "True", |
| "confidence": 0.99, |
| "evidence": "..." |
| } |
| ``` |
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| --- |
| |
| ## 📂 Project Structure |
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| ``` |
| TruthCheck/ |
| ├── app.py # Main Flask application & routes |
| ├── run.py # Entry point |
| ├── history.db # SQLite database (auto-created) |
| ├── models/ # AI Core |
| │ ├── claim_extractor.py # Identifies claims |
| │ ├── evidence_retriever.py # Web scraping logic |
| │ ├── keyword_extractor.py # NLP keyword extraction |
| │ └── nli_classifier.py # RoBERTa/DeBERTa inference pipeline |
| ├── static/ # Frontend Assets |
| │ ├── css/style.css # Custom animations & styles |
| │ └── js/main.js # Frontend logic |
| ├── templates/ # HTML Views |
| │ ├── index.html # Analyzer UI |
| │ ├── dashboard.html # Stats & History |
| │ ├── how_it_works.html # Architecture Docs |
| │ └── api.html # API Docs |
| └── utils/ # Helpers |
| └── config.py # App configuration |
| ``` |
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| --- |
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| ## 🤝 Contributing |
| Contributions are welcome! Please fork the repository and submit a Pull Request. |
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| ## 📄 License |
| This project is licensed under the MIT License. |
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