update README.md with detailed usage examples, API endpoints, and model information for NegaBot API

README.md

@@ -1,11 +1,262 @@
+# NegaBot API
+
+**Tweet Sentiment Classification using SmolLM 360M V2 Model**
+
+NegaBot is a sentiment analysis API that detects positive and negative sentiment in tweets, particularly focusing on product criticism detection. Built with FastAPI and the `jatinmehra/NegaBot-Product-Criticism-Catcher` model.
+
+## Features
+
+- **Advanced AI Model**: Uses fine-tuned SmolLM 360M V2 for accurate sentiment classification; Trained on real tweets data and can detect negative comments with sarcasm.
+- **Fast API**: RESTful API built with FastAPI for high-performance predictions
+- **Data Logging**: SQLite database for storing and analyzing predictions
+- **Batch Processing**: Support for single and batch predictions
+- **Built-in Dashboard**: HTML analytics dashboard with charts
+- **Data Export**: Download predictions as CSV or JSON
+
+## Quick Start
+
+1. **Install Dependencies**
+```bash
+pip install -r requirements.txt
+```
+
+2. **Start the API**
+```bash
+uvicorn api:app --host 0.0.0.0 --port 8000
+```
+
+3. **Access the Services**
+- API Documentation: http://localhost:8000/docs
+- Analytics Dashboard: http://localhost:8000/dashboard
+
+## Usage Examples
+
+### API Usage
+
+#### Single Prediction
+```bash
+curl -X POST "http://localhost:8000/predict" \
+     -H "Content-Type: application/json" \
+     -d '{"text": "This product is amazing! Best purchase ever!"}'
+```
+
+#### Batch Prediction
+```bash
+curl -X POST "http://localhost:8000/batch_predict" \
+     -H "Content-Type: application/json" \
+     -d '{
+       "tweets": [
+         "Amazing product, highly recommend!",
+         "Terrible quality, waste of money",
+         "Its okay, nothing special"
+       ]
+     }'
+```
+
+#### Python Client Example
+```python
+import requests
+
+# Single prediction
+response = requests.post(
+    "http://localhost:8000/predict",
+    json={"text": "This product broke after one week!"}
+)
+result = response.json()
+print(f"Sentiment: {result['sentiment']} (Confidence: {result['confidence']:.2%})")
+
+# Batch prediction
+response = requests.post(
+    "http://localhost:8000/batch_predict",
+    json={
+        "tweets": [
+            "Love this product!",
+            "Terrible experience",
+            "Pretty decent quality"
+        ]
+    }
+)
+results = response.json()
+for result in results['results']:
+    print(f"'{result['text']}' -> {result['sentiment']}")
+```
+
+### Model Usage (Direct)
+
+```python
+from model import NegaBotModel
+
+# Initialize model
+model = NegaBotModel()
+
+# Single prediction
+result = model.predict("This product is awful and broke within a week!")
+print(f"Sentiment: {result['sentiment']}")
+print(f"Confidence: {result['confidence']:.2%}")
+print(f"Probabilities: {result['probabilities']}")
+
+# Batch prediction
+texts = [
+    "Amazing quality, highly recommend!",
+    "Terrible customer service",
+    "Pretty good value for money"
+]
+results = model.batch_predict(texts)
+for result in results:
+    print(f"{result['text']} -> {result['sentiment']}")
+```
+
+## API Endpoints
+
+| Endpoint | Method | Description |
+|----------|--------|-------------|
+| `/` | GET | API information and available endpoints |
+| `/health` | GET | Health check and model status |
+| `/predict` | POST | Single tweet sentiment prediction |
+| `/batch_predict` | POST | Batch tweet sentiment prediction |
+| `/stats` | GET | Prediction statistics and analytics |
+| `/dashboard` | GET | HTML analytics dashboard |
+| `/dashboard/data` | GET | Dashboard data as JSON |
+| `/download/predictions.csv` | GET | Download predictions as CSV |
+| `/download/predictions.json` | GET | Download predictions as JSON |
+
+### Request/Response Schemas
+
+#### Predict Request
+```json
+{
+  "text": "string (1-1000 chars)",
+  "metadata": {
+    "optional": "metadata object"
+  }
+}
+```
+
+#### Predict Response
+```json
+{
+  "text": "input text",
+  "sentiment": "Positive|Negative",
+  "confidence": 0.95,
+  "predicted_class": 0,
+  "probabilities": {
+    "positive": 0.95,
+    "negative": 0.05
+  },
+  "timestamp": "2024-01-01T12:00:00"
+}
+```
+
+## Dashboard Features
+
+The built-in analytics dashboard provides:
+
+- **Real-time Metrics**: Total predictions, sentiment distribution, average confidence
+- **Interactive Charts**: Pie charts showing sentiment distribution
+- **Recent Predictions**: View latest prediction results
+- **Data Export**: Download prediction data as CSV or JSON
+- **Auto-refresh**: View updated statistics as new predictions are made
+
+## Testing
+
+Test the API using the interactive documentation at http://localhost:8000/docs or use curl commands as shown in the usage examples above.
+
+## Project Structure
+
+```
+NegaBot-API/
+├── api.py               # FastAPI application
+├── model.py             # NegaBot model wrapper
+├── database.py          # SQLite database and logging
+├── requirements.txt     # Python dependencies
+├── Dockerfile           # Docker configuration
+├── README.md           # This file
+└── negabot_predictions.db # Database (created at runtime)
+```
+
+## Configuration
+
+The API runs on port 8000 by default. You can modify the host and port by updating the uvicorn command:
+
+```bash
+uvicorn api:app --host 127.0.0.1 --port 8080
+```
+
+## Model Information
+
+- **Model**: `jatinmehra/NegaBot-Product-Criticism-Catcher`
+- **Base Architecture**: SmolLM 360M V2
+- **Task**: Binary sentiment classification
+- **Classes**: 
+  - 0: Positive sentiment
+  - 1: Negative sentiment (criticism/complaints)
+- **Input**: Text (max 512 tokens)
+- **Output**: Sentiment label + confidence scores
+
+### Performance Considerations
+
+- **Memory Requirements**: Model requires ~2GB RAM minimum
+- **API Scaling**: Use multiple worker processes with Gunicorn for production
+- **Database**: Current SQLite setup is suitable for development and small-scale production
+
+## Logging and Monitoring
+
+### Database Schema
+
+```sql
+CREATE TABLE predictions (
+    id INTEGER PRIMARY KEY AUTOINCREMENT,
+    text TEXT NOT NULL,
+    sentiment TEXT NOT NULL,
+    confidence REAL NOT NULL,
+    predicted_class INTEGER NOT NULL,
+    timestamp TEXT NOT NULL,
+    metadata TEXT,
+    created_at DATETIME DEFAULT CURRENT_TIMESTAMP
+);
+```
+
+### Log Files
+
+- Application logs: Console output
+- Prediction logs: SQLite database
+- Access logs: Uvicorn/Gunicorn logs
+
+## Contributing
+
+1. Fork the repository
+2. Create a feature branch
+3. Add tests for new features
+4. Ensure all tests pass
+5. Submit a pull request
+
+## License
+
+This project is licensed under the Apache-2.0 License - see the [LICENSE](LICENSE) file for details.
+
+## Troubleshooting
+
+### Common Issues
+
+1. **Model Loading Errors**
+   - Ensure internet connection for downloading the model
+   - Check disk space (model is ~1.5GB)
+   - Verify transformers library version
+
+2. **Port Conflicts**
+   - Change ports using command line arguments
+   - Check if port 8000 is already in use
+
+3. **Database Permissions**
+   - Ensure write permissions in the project directory
+   - Check SQLite installation
+
+4. **Memory Issues**
+   - Model requires ~2GB RAM minimum
+   - Consider using CPU-only inference for smaller systems
+
 ---
-title: NegaBot API
-emoji: 🔥
-colorFrom: purple
-colorTo: indigo
-sdk: docker
-pinned: false
-license: apache-2.0
----
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+**Built with FastAPI and the powerful NegaBot model.**
+
+Model used in this app-https://github.com/Jatin-Mehra119/NegaBot-Product-Criticism-Catcher