Update app.py

app.py

@@ -101,22 +101,23 @@ class CustomClient(fl.client.NumPyClient):
         return float(loss), len(self.testloader.dataset), {"accuracy": float(accuracy)}
 
     def plot_metrics(self, round_num):
-        st.write(f"#### Client {self.client_id} Metrics for Round {round_num}")
-        st.write(f"Loss: {self.losses[-1]:.4f}")
-        st.write(f"Accuracy: {self.accuracies[-1]:.4f}")
+        if self.losses and self.accuracies:
+            st.write(f"#### Client {self.client_id} Metrics for Round {round_num}")
+            st.write(f"Loss: {self.losses[-1]:.4f}")
+            st.write(f"Accuracy: {self.accuracies[-1]:.4f}")
 
-        fig, ax1 = plt.subplots()
+            fig, ax1 = plt.subplots()
 
-        ax2 = ax1.twinx()
-        ax1.plot(range(1, len(self.losses) + 1), self.losses, 'g-')
-        ax2.plot(range(1, len(self.accuracies) + 1), self.accuracies, 'b-')
+            ax2 = ax1.twinx()
+            ax1.plot(range(1, len(self.losses) + 1), self.losses, 'g-')
+            ax2.plot(range(1, len(self.accuracies) + 1), self.accuracies, 'b-')
 
-        ax1.set_xlabel('Round')
-        ax1.set_ylabel('Loss', color='g')
-        ax2.set_ylabel('Accuracy', color='b')
+            ax1.set_xlabel('Round')
+            ax1.set_ylabel('Loss', color='g')
+            ax2.set_ylabel('Accuracy', color='b')
 
-        plt.title(f'Client {self.client_id} Metrics')
-        st.pyplot(fig)
+            plt.title(f'Client {self.client_id} Metrics')
+            st.pyplot(fig)
 
 def main():
     st.write("## Federated Learning with Dynamic Models and Datasets for Mobile Devices")
@@ -198,6 +199,7 @@ if __name__ == "__main__":
 
 
 
+
 # # %%writefile app.py
 
 # import streamlit as st