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README.md

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-# PulmoScanAI - AI Lung Cancer Detection System
-
-An advanced web-based application for detecting lung cancer from histopathology images using a deep learning model trained on 100,000+ samples.
-
-## Features
-
-- **Real-time AI Analysis**: Uses TensorFlow/Keras deep learning model for accurate cancer detection
-- **Beautiful UI**: Modern, responsive design with animated backgrounds
-- **Drag & Drop Upload**: Easy image upload with preview
-- **Confidence Score**: Displays detection confidence percentage
-- **CORS Enabled**: Seamless frontend-backend communication
-
-## Setup & Installation
-
-### Prerequisites
-- Python 3.8 or higher
-- pip (Python package manager)
-
-### Step 1: Install Dependencies
-
-```bash
-pip install -r requirements.txt
-```
-
-### Step 2: Verify Model File
-
-Ensure `best_lung_model.h5` is in the same directory as `app.py`.
-
-```
-c:\Users\debja\Desktop\ayushman\
-├── app.py
-├── PulmoScanAI.html
-├── best_lung_model.h5
-├── requirements.txt
-└── README.md
-```
-
-### Step 3: Run the Backend Server
-
-```bash
-python app.py
-```
-
-You'll see output:
-```
-Starting PulmoScanAI server on http://127.0.0.1:5000
-```
-
-### Step 4: Access the Frontend
-
-Open your browser and navigate to:
-```
-http://127.0.0.1:5000
-```
-
-Or open `PulmoScanAI.html` directly in your browser and the page will communicate with the backend at `http://127.0.0.1:5000/api/predict`.
-
-## API Endpoints
-
-### Health Check
-```
-GET http://127.0.0.1:5000/api/health
-```
-
-Response:
-```json
-{
-  "status": "ok",
-  "model_loaded": true
-}
-```
-
-### Prediction
-```
-POST http://127.0.0.1:5000/api/predict
-```
-
-**Request**: Multipart form data with `image` file
-**Response**:
-```json
-{
-  "is_cancer": false,
-  "confidence": 0.92,
-  "diagnosis": "No Cancer Found",
-  "confidence_percentage": 92.0
-}
-```
-
-## How It Works
-
-1. **Frontend**: User uploads a histopathology image via the web interface
-2. **Preprocessing**: Image is resized to 256×256 and normalized
-3. **Model Inference**: TensorFlow model processes the image
-4. **Result**: Confidence score and diagnosis displayed with color-coded box:
-   - **Green border**: Normal (no cancer detected)
-   - **Red border**: Cancer detected
-
-## Customization
-
-### Adjust Model Input Size
-If your model expects a different input size, edit `app.py`:
-```python
-image = image.resize((224, 224))  # Change 256, 256 to your model's size
-```
-
-### Modify Classification Threshold
-To change the cancer/normal threshold:
-```python
-is_cancer = cancer_prob > 0.5  # Change 0.5 to your preferred threshold
-```
-
-## Troubleshooting
-
-**Error: Model not loaded**
-- Ensure `best_lung_model.h5` exists in the same directory
-- Check TensorFlow installation: `pip install --upgrade tensorflow`
-
-**CORS errors**
-- Flask-CORS is enabled. If issues persist, check browser console for details
-
-**Image processing fails**
-- Ensure uploaded image is JPG, PNG, or TIFF format
-- File size should be under 20MB
-
-## Model Information
-
-- **Architecture**: Deep Convolutional Neural Network
-- **Training Data**: 100,000+ histopathology samples
-- **Input**: 256×256 RGB images
-- **Output**: Binary classification (Cancer/Normal) with confidence score
-
-## Technical Stack
-
-- **Frontend**: HTML5, CSS3, JavaScript (Vanilla)
-- **Backend**: Python Flask with Flask-CORS
-- **ML Framework**: TensorFlow 2.x / Keras
-- **Image Processing**: OpenCV, Pillow
-
-## License
-
-© 2025 PulmoScanAI • Next-Gen AI Pathology Platform
-
-## Support
-
-For issues or questions, please review the error messages in the browser console (F12) and Flask terminal output.