Prepare NeuroScan for Railway deployment: Add CORS support, environment-based config, and comprehensive deployment documentation

.env.example

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+# Backend Environment Variables (for Railway/Production)
+# Copy this file and set your actual values
+
+# Required: Google Gemini API Key
+# Get one from: https://makersuite.google.com/app/apikey
+GOOGLE_API_KEY=your_gemini_api_key_here
+
+# Optional: Port (Railway sets this automatically)
+PORT=5050
+
+# Optional: Upload folder path
+UPLOAD_FOLDER=Uploads
+
+# Optional: Dataset path (only needed for /random endpoint)
+DATASET_PATH=./Dataset
+
+# Optional: Model file path
+MODEL_PATH=mobilenet_brain_tumor_classifier.h5

.env.local.example

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+# Frontend Environment Variables (Next.js)
+# Copy this file to .env.local and set your actual Railway backend URL
+
+# Backend API URL from Railway
+NEXT_PUBLIC_API_URL=https://your-railway-backend.railway.app
+
+# Note: Replace 'your-railway-backend.railway.app' with your actual Railway deployment URL
+# You can find this in your Railway project dashboard after deployment

CHANGES_SUMMARY.md

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+# Changes Summary - Railway Deployment Preparation
+
+This document summarizes all changes made to prepare the NeuroScan application for deployment on Railway.
+
+## 🔧 Code Changes
+
+### 1. server1.py
+
+#### Added CORS Support
+```python
+from flask_cors import CORS
+
+app = Flask(__name__)
+CORS(app)  # Enable CORS for all routes
+```
+- **Why**: Allows the frontend (deployed on Vercel/Netlify) to make API requests to the backend (deployed on Railway)
+- **Impact**: No more CORS errors when accessing the API from different domains
+
+#### Updated Gemini API Configuration
+```python
+# Before:
+genai.configure(api_key='Add Your Own APi Key')
+
+# After:
+api_key = os.getenv('GOOGLE_API_KEY')
+if api_key:
+    genai.configure(api_key=api_key)
+    gemini_vision_model = genai.GenerativeModel('gemini-2.5-flash-preview-05-20')
+else:
+    logger.warning("GOOGLE_API_KEY environment variable not set.")
+    gemini_vision_model = None
+```
+- **Why**: Security best practice - never hardcode API keys
+- **Impact**: API key is now read from environment variables, making it secure and configurable per deployment
+
+#### Port Configuration
+```python
+# Already present, but verified:
+port = int(os.environ.get("PORT", 5050))
+app.run(debug=True, host='0.0.0.0', port=port)
+```
+- **Why**: Railway automatically sets the PORT environment variable
+- **Impact**: Application will work correctly on Railway without hardcoded ports
+
+### 2. Dockerfile
+
+#### Updated CMD to Use Gunicorn
+```dockerfile
+# Before:
+CMD ["python", "server1.py"]
+
+# After:
+CMD gunicorn --bind 0.0.0.0:${PORT:-5050} --timeout 120 server1:app
+```
+- **Why**: Gunicorn is a production-grade WSGI server, better than Flask's development server
+- **Impact**: Improved performance, stability, and proper handling of multiple requests
+
+### 3. requirements.txt
+
+#### Verified Dependencies
+All required dependencies are already present:
+- ✅ Flask==2.3.3
+- ✅ Flask-Cors==4.0.0
+- ✅ gunicorn==20.1.0
+- ✅ numpy==1.24.3
+- ✅ tensorflow==2.15.0
+- ✅ pillow==11.2.1
+- ✅ opencv-python==4.8.0.76
+- ✅ google-generativeai==0.8.5
+- ✅ Werkzeug==3.1.3
+
+**No changes needed** - all dependencies were already correctly specified.
+
+## 📚 Documentation Changes
+
+### 4. README.md
+
+#### Added Backend Deployment Section
+- Comprehensive Railway deployment guide
+- Prerequisites and deployment steps
+- Environment variable configuration
+- External storage options for large files (GCS, S3)
+- Alternative deployment platforms
+
+#### Enhanced Backend Integration Section
+- Frontend environment configuration (`.env.local`)
+- API endpoint documentation
+- CORS configuration explanation
+- Example API usage code
+
+### 5. DEPLOYMENT.md (New File)
+
+Created a detailed deployment guide covering:
+- Step-by-step Railway deployment instructions
+- Environment variable reference table
+- Frontend configuration
+- Large file handling strategies (Git LFS, GCS, S3, Railway volumes)
+- Troubleshooting common issues
+- Security best practices
+- Cost considerations
+- Monitoring and support resources
+
+### 6. .env.example (New File)
+
+Created backend environment variables template:
+```env
+GOOGLE_API_KEY=your_gemini_api_key_here
+PORT=5050
+UPLOAD_FOLDER=Uploads
+DATASET_PATH=./Dataset
+MODEL_PATH=mobilenet_brain_tumor_classifier.h5
+```
+
+### 7. .env.local.example (New File)
+
+Created frontend environment variables template:
+```env
+NEXT_PUBLIC_API_URL=https://your-railway-backend.railway.app
+```
+
+## 🎯 What This Accomplishes
+
+### Security ✅
+- Removed hardcoded API key
+- API keys now managed via environment variables
+- Better secret management for production
+
+### Functionality ✅
+- CORS support for cross-origin requests
+- Frontend can communicate with backend on different domains
+- Production-ready server with Gunicorn
+
+### Deployment ✅
+- Railway-ready configuration
+- Dynamic PORT handling
+- Proper environment variable usage
+- Comprehensive documentation
+
+### Developer Experience ✅
+- Clear deployment instructions
+- Environment variable templates
+- Troubleshooting guide
+- Multiple deployment options
+
+## 🚀 Next Steps for Deployment
+
+1. **Backend (Railway)**:
+   - Push code to GitHub
+   - Create Railway project from repository
+   - Set `GOOGLE_API_KEY` environment variable
+   - Railway will automatically build and deploy
+
+2. **Frontend (Vercel/Netlify)**:
+   - Create `.env.local` with Railway backend URL
+   - Deploy to Vercel or Netlify
+   - Application is now fully functional!
+
+3. **Large Files**:
+   - Decide on storage strategy for `mobilenet_brain_tumor_classifier.h5`
+   - Options: Git LFS, GCS, S3, or Railway volume
+   - Follow instructions in DEPLOYMENT.md
+
+## 📋 Testing Checklist
+
+Before going live, verify:
+- [ ] Backend deploys successfully on Railway
+- [ ] Environment variables are set correctly
+- [ ] Frontend can connect to backend
+- [ ] Image upload and prediction works
+- [ ] Grad-CAM heatmap generation works
+- [ ] CORS is working (no browser console errors)
+- [ ] Gemini API is responding (if key is set)
+
+## 🐛 Known Considerations
+
+1. **Model File Size**: The model file (~150MB) may need Git LFS or external storage
+2. **Dataset Files**: Optional for production; only needed for `/random` endpoint
+3. **Gemini API**: Optional; application works without it (MRI validation disabled)
+4. **Cold Starts**: Railway may have cold starts on free tier (first request slower)
+
+## 📦 Files Modified
+
+1. `server1.py` - Added CORS, environment-based config
+2. `Dockerfile` - Updated to use Gunicorn with PORT variable
+3. `README.md` - Added deployment sections
+
+## 📦 Files Added
+
+1. `DEPLOYMENT.md` - Comprehensive deployment guide
+2. `.env.example` - Backend environment template
+3. `.env.local.example` - Frontend environment template
+4. `CHANGES_SUMMARY.md` - This file
+
+## 📞 Support
+
+If you need help with deployment:
+1. Check DEPLOYMENT.md for detailed instructions
+2. Review Railway logs for any errors
+3. Verify environment variables are set correctly
+4. Open an issue on GitHub if problems persist
+
+---
+
+**Summary**: All requirements from the problem statement have been successfully implemented. The application is now ready for deployment on Railway with proper security, CORS support, and comprehensive documentation.