Create app.py

app.py

@@ -0,0 +1,41 @@
+import gradio as gr
+import joblib
+import numpy as np
+
+# Load the scaler and models
+scaler = joblib.load('scaler.joblib')
+models = {
+    "processing": joblib.load('svm_model_processing.joblib'),
+    "perception": joblib.load('svm_model_perception.joblib'),
+    "input": joblib.load('svm_model_input.joblib'),
+    "understanding": joblib.load('svm_model_understanding.joblib'),
+}
+
+# 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]
+
+    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)
+
+# Launch the interface
+interface.launch(share=True)