Update app.py

app.py

@@ -13,29 +13,22 @@ 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)
-
-    # Predict outcomes for all target variables
-    predictions = {}
-    for target, model in models.items():
-        prediction = model.predict(user_input_scaled)
-        predictions[target] = prediction[0]
-
+    predictions = {target: model.predict(user_input_scaled)[0] for target, model in models.items()}
     return predictions
 
-# 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 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
+)
 
-# Launch the interface
-interface.launch()
+# Define the callable object for inference
+model = interface