Update app.py

app.py

@@ -13,22 +13,29 @@ models = {
 
 # Define the prediction function
 def predict(user_input):
+    # Ensure the input is in the same order as your model expects
     user_input_array = np.array(user_input).reshape(1, -1)
+
+    # Scale the input using the saved scaler
     user_input_scaled = scaler.transform(user_input_array)
-    predictions = {target: model.predict(user_input_scaled)[0] for target, model in models.items()}
+
+    # Predict outcomes for all target variables
+    predictions = {}
+    for target, model in models.items():
+        prediction = model.predict(user_input_scaled)
+        predictions[target] = prediction[0]
+
     return predictions
 
-# Define the interface
-interface = gr.Interface(
-    fn=predict,
-    inputs=gr.Dataframe(type="numpy", row_count=1, col_count=12, 
-                        headers=["course overview", "reading file", "abstract materiale",
-                                 "concrete material", "visual materials", "self-assessment",
-                                 "exercises submit", "quiz submitted", "playing", "paused",
-                                 "unstarted", "buffering"]),
-    outputs=gr.JSON(),
-    live=True
-)
+# Define Gradio interface
+interface = gr.Interface(fn=predict, 
+                         inputs=gr.Dataframe(type="numpy", row_count=1, col_count=12, 
+                                             headers=['course overview', 'reading file', 'abstract materiale', 
+                                                      'concrete material', 'visual materials', 'self-assessment', 
+                                                      'exercises submit', 'quiz submitted', 'playing', 'paused', 
+                                                      'unstarted', 'buffering']),
+                         outputs=gr.JSON(),
+                         live=True)
 
-# Define the callable object for inference
-model = interface
+# Launch the interface
+interface.launch()