Update app.py

app.py

@@ -15,6 +15,7 @@ os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'
 # Load model artifacts
 def load_model_artifacts():
     try:
+        # Load from the same directory where training code saved artifacts
         with open('model_architecture.json', 'r') as json_file:
             model_json = json_file.read()
         model = model_from_json(model_json)
@@ -33,21 +34,26 @@ def load_model_artifacts():
 # Initialize model components
 try:
     model, scaler, metadata = load_model_artifacts()
-    # Use only two features for prediction
-    feature_names = ['Feature_1', 'Feature_2']
+    feature_names = metadata['feature_names']  # Get feature names from metadata
     print(f"✅ Model loaded successfully with features: {feature_names}")
 except Exception as e:
     print(f"❌ Error loading model: {e}")
     model, scaler, metadata = None, None, {}
-    feature_names = ['Feature_1', 'Feature_2']
+    feature_names = ['Feature_1', 'Feature_2']  # Fallback if metadata not available
 
 def predict_student_eligibility(*args):
     try:
         if model is None or scaler is None:
             return "Model not loaded", "N/A", "N/A", create_error_plot()
 
+        # Create input dictionary with correct feature names
         input_data = {feature_names[i]: args[i] for i in range(len(feature_names))}
         input_df = pd.DataFrame([input_data])
+        
+        # Ensure columns are in correct order
+        input_df = input_df[feature_names]
+        
+        # Scale and reshape input
         input_scaled = scaler.transform(input_df)
         input_reshaped = input_scaled.reshape(input_scaled.shape[0], input_scaled.shape[1], 1)
 
@@ -85,6 +91,7 @@ def create_prediction_viz(probability, prediction, input_data):
                    [{"type": "bar"}, {"type": "scatter"}]]
         )
 
+        # Prediction probability gauge
         fig.add_trace(
             go.Indicator(
                 mode="gauge+number",
@@ -106,6 +113,7 @@ def create_prediction_viz(probability, prediction, input_data):
             ), row=1, col=1
         )
 
+        # Confidence meter
         confidence = abs(probability - 0.5) * 2
         fig.add_trace(
             go.Indicator(
@@ -124,27 +132,46 @@ def create_prediction_viz(probability, prediction, input_data):
             ), row=1, col=2
         )
 
+        # Input features bar chart
         features = list(input_data.keys())
         values = list(input_data.values())
-        fig.add_trace(go.Bar(x=features, y=values, name="Input Values", marker_color="skyblue"), row=2, col=1)
+        fig.add_trace(
+            go.Bar(
+                x=features, 
+                y=values, 
+                name="Input Values", 
+                marker_color="skyblue",
+                text=values,
+                textposition='auto'
+            ), 
+            row=2, col=1
+        )
 
+        # Probability distribution
         fig.add_trace(
             go.Scatter(
-                x=[0, 1], y=[probability, probability],
+                x=[0, 1], 
+                y=[probability, probability],
                 mode='lines+markers',
                 name="Probability",
                 line=dict(color="red", width=3),
                 marker=dict(size=10)
-            ), row=2, col=2
+            ), 
+            row=2, col=2
         )
 
         fig.update_layout(
             height=800,
             showlegend=False,
             title_text="Student Eligibility Prediction Dashboard",
-            title_x=0.5
+            title_x=0.5,
+            margin=dict(l=50, r=50, t=100, b=50)
         )
 
+        # Update x-axis for probability plot
+        fig.update_xaxes(title_text="", row=2, col=2, range=[-0.1, 1.1])
+        fig.update_yaxes(title_text="Probability", row=2, col=2, range=[0, 1])
+
         return fig
     except Exception as e:
         return create_error_plot()
@@ -158,10 +185,13 @@ def batch_predict(file):
             return "Please upload a CSV file.", None
 
         df = pd.read_csv(file)
+        
+        # Check for required features
         missing_features = set(feature_names) - set(df.columns)
         if missing_features:
-            return f"Missing features: {missing_features}", None
+            return f"Missing features: {', '.join(missing_features)}", None
 
+        # Ensure correct column order
         df_features = df[feature_names]
         df_scaled = scaler.transform(df_features)
         df_reshaped = df_scaled.reshape(df_scaled.shape[0], df_scaled.shape[1], 1)
@@ -169,7 +199,7 @@ def batch_predict(file):
         probabilities = model.predict(df_reshaped).flatten()
         predictions = ["Eligible" if p > 0.5 else "Not Eligible" for p in probabilities]
 
-        results_df = df_features.copy()
+        results_df = df.copy()
         results_df['Probability'] = probabilities
         results_df['Prediction'] = predictions
         results_df['Confidence'] = np.abs(probabilities - 0.5) * 2
@@ -197,27 +227,52 @@ Results saved to: {output_file}
         return f"Error processing file: {str(e)}", None
 
 # Gradio UI
-demo = gr.Blocks(theme=gr.themes.Soft())
-
-with demo:
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
     gr.Markdown("# 🎓 Student Eligibility Prediction")
+    gr.Markdown("This app predicts student eligibility based on academic performance metrics.")
+    
     with gr.Tabs():
-        with gr.Tab("Single Prediction"):
-            inputs = [gr.Number(label=feature, value=75) for feature in feature_names]
-            predict_btn = gr.Button("Predict")
+        with gr.Tab("📝 Single Prediction"):
             with gr.Row():
-                prediction = gr.Textbox(label="Prediction")
-                probability = gr.Textbox(label="Probability")
-                confidence = gr.Textbox(label="Confidence")
+                with gr.Column():
+                    inputs = [gr.Number(label=feature, value=75) for feature in feature_names]
+                    predict_btn = gr.Button("Predict", variant="primary")
+                with gr.Column():
+                    prediction = gr.Textbox(label="Prediction")
+                    probability = gr.Textbox(label="Probability")
+                    confidence = gr.Textbox(label="Confidence")
             plot = gr.Plot()
-            predict_btn.click(predict_student_eligibility, inputs=inputs, outputs=[prediction, probability, confidence, plot])
-
-        with gr.Tab("Batch Prediction"):
-            file_input = gr.File(label="Upload CSV", file_types=[".csv"], type="filepath")
-            batch_btn = gr.Button("Process Batch")
-            batch_output = gr.Textbox(label="Results")
-            download = gr.File(label="Download")
-            batch_btn.click(batch_predict, inputs=file_input, outputs=[batch_output, download])
+            
+            predict_btn.click(
+                predict_student_eligibility, 
+                inputs=inputs, 
+                outputs=[prediction, probability, confidence, plot]
+            )
+
+        with gr.Tab("📁 Batch Prediction"):
+            gr.Markdown("Upload a CSV file with student data to get batch predictions.")
+            with gr.Row():
+                with gr.Column():
+                    file_input = gr.File(
+                        label="Upload CSV", 
+                        file_types=[".csv"], 
+                        type="filepath"
+                    )
+                    batch_btn = gr.Button("Process Batch", variant="primary")
+                with gr.Column():
+                    batch_output = gr.Textbox(label="Results", lines=10)
+                    download = gr.File(label="Download Predictions")
+            
+            batch_btn.click(
+                batch_predict, 
+                inputs=file_input, 
+                outputs=[batch_output, download]
+            )
+
+    # Footer
+    gr.Markdown("---")
+    gr.Markdown("> Note: This model was trained on student eligibility data. Ensure your input features match the training data format.")
 
 # Launch app
-demo.launch()
+if __name__ == "__main__":
+    demo.launch()