fix: fixed server error

Author: priyansh-saxena1

DEPLOYMENT.md

@@ -117,4 +117,300 @@ After deployment, you'll have:
 - ✅ Professional presentation of your project
 - ✅ Educational value for visitors
 
-**Your federated learning demo will be live and working!** 🚀 
+**Your federated learning demo will be live and working!** 🚀 
+
+# FinFedRAG Deployment Guide
+
+## Overview
+
+This project implements a federated learning framework with RAG capabilities for financial data. The system can be deployed using Docker Compose for local development or Kubernetes for production environments.
+
+## Debugging and Monitoring
+
+### Enhanced Debugging Features
+
+The web application now includes comprehensive debugging capabilities:
+
+1. **Debug Information Panel**: Located in the sidebar, shows:
+   - Real-time server health status
+   - Recent debug messages and logs
+   - Connection error details
+   - Client simulator status
+
+2. **Detailed Error Logging**: All operations are logged with:
+   - Connection attempts and failures
+   - Server response details
+   - Timeout and network error handling
+   - Client registration and training status updates
+
+3. **Real-time Status Monitoring**: 
+   - Server health checks
+   - Training progress tracking
+   - Client connection status
+   - Error message history
+
+### Using the Debug Features
+
+1. **Enable Debug Mode**: Uncheck "Demo Mode" in the sidebar
+2. **View Debug Information**: Expand the "Debug Information" section in the sidebar
+3. **Monitor Logs**: Check the "Recent Logs" section for real-time updates
+4. **Clear Logs**: Use the "Clear Debug Logs" button to reset the log history
+
+## Local Development Setup
+
+### Prerequisites
+
+- Python 3.8+
+- Docker and Docker Compose
+- Required Python packages (see requirements.txt)
+
+### Quick Start
+
+1. **Clone and Setup**:
+   ```bash
+   git clone <repository-url>
+   cd FinFedRAG-Financial-Federated-RAG
+   python -m venv venv
+   source venv/bin/activate  # On Windows: venv\Scripts\activate
+   pip install -r requirements.txt
+   ```
+
+2. **Start the Federated Server**:
+   ```bash
+   python src/main.py --mode server
+   ```
+
+3. **Start Multiple Clients** (in separate terminals):
+   ```bash
+   python src/main.py --mode client --client-id client1
+   python src/main.py --mode client --client-id client2
+   python src/main.py --mode client --client-id client3
+   ```
+
+4. **Run the Web Application**:
+   ```bash
+   streamlit run app.py
+   ```
+
+### Docker Compose Deployment
+
+For containerized deployment:
+
+```bash
+cd docker
+docker-compose up --build
+```
+
+This will start:
+- 1 federated server on port 8000
+- 3 federated clients
+- All services connected via Docker network
+
+## Kubernetes Deployment
+
+### Architecture Overview
+
+The Kubernetes setup provides a production-ready deployment with:
+
+- **Server Deployment**: Single federated learning server
+- **Client Deployment**: Multiple federated learning clients (3 replicas)
+- **Service Layer**: Internal service discovery
+- **ConfigMaps**: Configuration management
+- **Namespace Isolation**: Dedicated `federated-learning` namespace
+
+### Components
+
+#### 1. Server Deployment (`kubernetes/deployments/server.yaml`)
+```yaml
+- Replicas: 1 (single server instance)
+- Port: 8000 (internal)
+- Config: Mounted from ConfigMap
+- Image: fl-server:latest
+```
+
+#### 2. Client Deployment (`kubernetes/deployments/client.yaml`)
+```yaml
+- Replicas: 3 (multiple client instances)
+- Environment: SERVER_HOST=fl-server-service
+- Config: Mounted from ConfigMap
+- Image: fl-client:latest
+```
+
+#### 3. Service (`kubernetes/services/service.yaml`)
+```yaml
+- Type: ClusterIP (internal communication)
+- Port: 8000
+- Selector: app=fl-server
+```
+
+### Deployment Steps
+
+1. **Build Docker Images**:
+   ```bash
+   docker build -f docker/Dockerfile.server -t fl-server:latest .
+   docker build -f docker/Dockerfile.client -t fl-client:latest .
+   ```
+
+2. **Create Namespace**:
+   ```bash
+   kubectl create namespace federated-learning
+   ```
+
+3. **Create ConfigMaps**:
+   ```bash
+   kubectl create configmap server-config --from-file=config/server_config.yaml -n federated-learning
+   kubectl create configmap client-config --from-file=config/client_config.yaml -n federated-learning
+   ```
+
+4. **Deploy Services**:
+   ```bash
+   kubectl apply -f kubernetes/services/service.yaml
+   kubectl apply -f kubernetes/deployments/server.yaml
+   kubectl apply -f kubernetes/deployments/client.yaml
+   ```
+
+5. **Verify Deployment**:
+   ```bash
+   kubectl get pods -n federated-learning
+   kubectl get services -n federated-learning
+   ```
+
+### Accessing the Application
+
+#### Option 1: Port Forwarding
+```bash
+kubectl port-forward service/fl-server-service 8080:8000 -n federated-learning
+```
+
+#### Option 2: Load Balancer
+Modify the service to use LoadBalancer type:
+```yaml
+apiVersion: v1
+kind: Service
+metadata:
+  name: fl-server-service
+  namespace: federated-learning
+spec:
+  type: LoadBalancer  # Changed from ClusterIP
+  selector:
+    app: fl-server
+  ports:
+  - port: 8080
+    targetPort: 8000
+```
+
+#### Option 3: Ingress Controller
+Create an ingress resource for external access:
+```yaml
+apiVersion: networking.k8s.io/v1
+kind: Ingress
+metadata:
+  name: fl-ingress
+  namespace: federated-learning
+spec:
+  rules:
+  - host: fl.example.com
+    http:
+      paths:
+      - path: /
+        pathType: Prefix
+        backend:
+          service:
+            name: fl-server-service
+            port:
+              number: 8000
+```
+
+### Monitoring and Debugging in Kubernetes
+
+1. **View Pod Logs**:
+   ```bash
+   kubectl logs -f deployment/fl-server -n federated-learning
+   kubectl logs -f deployment/fl-client -n federated-learning
+   ```
+
+2. **Check Pod Status**:
+   ```bash
+   kubectl describe pods -n federated-learning
+   ```
+
+3. **Access Pod Shell**:
+   ```bash
+   kubectl exec -it <pod-name> -n federated-learning -- /bin/bash
+   ```
+
+4. **Monitor Resource Usage**:
+   ```bash
+   kubectl top pods -n federated-learning
+   ```
+
+## Troubleshooting
+
+### Common Issues
+
+1. **Connection Refused Errors**:
+   - Check if server is running: `kubectl get pods -n federated-learning`
+   - Verify service exists: `kubectl get services -n federated-learning`
+   - Check pod logs for startup errors
+
+2. **Client Registration Failures**:
+   - Ensure server is healthy before starting clients
+   - Check network connectivity between pods
+   - Verify ConfigMap configurations
+
+3. **Training Status Issues**:
+   - Monitor server logs for aggregation errors
+   - Check client participation in training rounds
+   - Verify model update sharing
+
+### Debug Commands
+
+```bash
+# Check all resources in namespace
+kubectl get all -n federated-learning
+
+# View detailed pod information
+kubectl describe pod <pod-name> -n federated-learning
+
+# Check service endpoints
+kubectl get endpoints -n federated-learning
+
+# View ConfigMap contents
+kubectl get configmap server-config -n federated-learning -o yaml
+```
+
+## Production Considerations
+
+1. **Resource Limits**: Add resource requests and limits to deployments
+2. **Health Checks**: Implement liveness and readiness probes
+3. **Secrets Management**: Use Kubernetes secrets for sensitive data
+4. **Persistent Storage**: Add persistent volumes for model storage
+5. **Monitoring**: Integrate with Prometheus/Grafana for metrics
+6. **Logging**: Use centralized logging (ELK stack, Fluentd)
+
+## Scaling
+
+### Horizontal Pod Autoscaling
+```yaml
+apiVersion: autoscaling/v2
+kind: HorizontalPodAutoscaler
+metadata:
+  name: fl-client-hpa
+  namespace: federated-learning
+spec:
+  scaleTargetRef:
+    apiVersion: apps/v1
+    kind: Deployment
+    name: fl-client
+  minReplicas: 3
+  maxReplicas: 10
+  metrics:
+  - type: Resource
+    resource:
+      name: cpu
+      target:
+        type: Utilization
+        averageUtilization: 70
+```
+
+This deployment guide provides comprehensive information for both local development and production Kubernetes deployment, with enhanced debugging capabilities for better monitoring and troubleshooting.