Update app.py

app.py

@@ -1,5 +1,7 @@
 import streamlit as st
 import pandas as pd
+import seaborn as sns
+from scipy.stats import boxcox
 import numpy as np
 import plotly.express as px
 import plotly.graph_objects as go
@@ -1335,69 +1337,78 @@ elif app_mode == "Model Training":
             num_trials = st.number_input("Number of Trials", 1, 100, 10, help="Number of trials for hyperparameter search.")
 
     # ----- [5. Training & Monitoring] -----
-    st.subheader("🎯 Training Configuration")
-    
-    class LiveMetrics(Callback):
-        def on_epoch_end(self, epoch, logs=None):
-            if 'metrics' not in st.session_state:
-                st.session_state.metrics = []
-            st.session_state.metrics.append(logs)
-            self.update_chart()
-            
-        def update_chart(self):
-            df = pd.DataFrame(st.session_state.metrics)
-            fig = px.line(df, y=['loss', 'val_loss'], 
-                          title="Training Progress")
-            loss_chart.plotly_chart(fig)
-    
-    if st.button("🚀 Start Training"):
-        try:
-            model = tf.keras.Sequential()
-            
-            # Add layers with regularization
-            for layer in st.session_state.layers:
-                layer_class = {
-                    "Dense": Dense,
-                    "Conv2D": Conv2D,
-                    "LSTM": LSTM
-                }[layer['type']]
-                
-                # Add regularization
-                if l2_reg > 0:
-                    layer['kernel_regularizer'] = tf.keras.regularizers.l2(l2_reg)
-                
-                model.add(layer_class(**layer))
-                
-                # Add batch norm after each layer
-                if batch_norm:
-                    model.add(BatchNormalization())
-                    
-                # Add global dropout
-                if dropout > 0:
-                    model.add(Dropout(dropout))
-            
-            model.compile(
-                optimizer=optimizer,
-                loss=loss,
-                metrics=metrics
-            )
+st.subheader("🎯 Training Configuration")
+
+import shap  # Ensure SHAP is installed: pip install shap
+
+class LiveMetrics(Callback):
+    def on_epoch_end(self, epoch, logs=None):
+        if 'metrics' not in st.session_state:
+            st.session_state.metrics = []
+        st.session_state.metrics.append(logs)
+        self.update_chart()
+        
+    def update_chart(self):
+        df = pd.DataFrame(st.session_state.metrics)
+        fig = px.line(df, y=['loss', 'val_loss'], title="Training Progress")
+        loss_chart.plotly_chart(fig)
+
+if st.button("🚀 Start Training"):
+    try:
+        model = tf.keras.Sequential()
+        
+        # Add layers with regularization
+        for layer in st.session_state.layers:
+            layer_class = {
+                "Dense": Dense,
+                "Conv2D": Conv2D,
+                "LSTM": LSTM
+            }[layer['type']]
             
-            # Show model summary
-            st.subheader("Model Architecture")
-            with tempfile.NamedTemporaryFile(suffix='.png') as tmp:
-                plot_model(model, to_file=tmp.name, show_shapes=True)
-                st.image(tmp.name)
+            # Add regularization
+            if l2_reg > 0:
+                layer['kernel_regularizer'] = tf.keras.regularizers.l2(l2_reg)
             
-            # Start training
-            st.subheader("Live Training Metrics")
-            loss_chart = st.empty()
-            model.fit(X_train, y_train, 
-                      epochs=10, 
-                      validation_data=(X_val, y_val),
-                      callbacks=[LiveMetrics()])
+            model.add(layer_class(**layer))
             
-        except Exception as e:
-            st.error(f"Training failed: {str(e)}")
+            # Add batch norm after each layer
+            if batch_norm:
+                model.add(BatchNormalization())
+                
+            # Add global dropout
+            if dropout > 0:
+                model.add(Dropout(dropout))
+        
+        model.compile(
+            optimizer=optimizer,
+            loss=loss,
+            metrics=metrics
+        )
+        
+        # Show model summary
+        st.subheader("Model Architecture")
+        with tempfile.NamedTemporaryFile(suffix='.png') as tmp:
+            plot_model(model, to_file=tmp.name, show_shapes=True)
+            st.image(tmp.name)
+        
+        # Start training
+        st.subheader("Live Training Metrics")
+        loss_chart = st.empty()
+        model.fit(X_train, y_train, 
+                  epochs=10, 
+                  validation_data=(X_val, y_val),
+                  callbacks=[LiveMetrics()])
+        
+        # SHAP explanations
+        st.subheader("SHAP Explanations")
+        explainer = shap.KernelExplainer(model.predict, X_train[:100])
+        shap_values = explainer.shap_values(X_train[:100])
+        shap.summary_plot(shap_values, X_train[:100], plot_type="bar")
+        st.pyplot(bbox_inches='tight')
+        
+    except Exception as e:
+        st.error(f"Training failed: {str(e)}")
+
 
 
     # ----- [6. Export & Deployment] -----
@@ -1501,13 +1512,17 @@ if app_mode == "Predictions":
 
             if problem_type == "Classification":
                 st.metric("Predicted Class", str(prediction))
+            elif problem_type == "Binary Classification":
+                st.metric("Predicted Probability", f"{prediction:.2f}")
+                binary_class = "Yes" if prediction >= 0.5 else "No"
+                st.metric("Binary Class", binary_class)
             else:
                 st.metric("Predicted Value", f"{prediction:.2f}")
 
             # 8. Feature Explanation (SHAP)
             enhance_section_title("Insights", "💡")
 
-            if problem_type == "Classification":
+            if problem_type == "Classification" or problem_type == "Binary Classification":
                 explainer = shap.TreeExplainer(model)
                 shap_values = explainer.shap_values(scaled_input)
                 fig = shap.force_plot(explainer.expected_value[1], shap_values[1], input_df, matplotlib=False, link="logit")
@@ -1534,4 +1549,4 @@ if app_mode == "Predictions":
                 st.warning(f"Could not calculate permutation feature importance: {e}")
 
         except Exception as e:
-            st.error(f"Prediction failed: {str(e)}")
+            st.error(f"Prediction failed: {str(e)}")