Update app.py

app.py

@@ -1,36 +1,41 @@
-from flask import Flask, request, jsonify
+import gradio as gr
 import joblib
-import pandas as pd
+import numpy as np
 
-app = Flask(__name__)
-
-# Load models and scaler
+# 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"),
+    "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'),
 }
-scaler = joblib.load("scaler.joblib")
 
-@app.route("/predict", methods=["POST"])
-def predict():
-    try:
-        # Parse input data from JSON
-        input_data = request.json
-        df = pd.DataFrame([input_data])
+# 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)
 
-        # Scale the data
-        df_scaled = scaler.transform(df)
+    # Predict outcomes for all target variables
+    predictions = {}
+    for target, model in models.items():
+        prediction = model.predict(user_input_scaled)
+        predictions[target] = prediction[0]
 
-        # Make predictions for all target variables
-        predictions = {}
-        for target, model in models.items():
-            predictions[target] = model.predict(df_scaled)[0]
+    return predictions
 
-        return jsonify({"success": True, "predictions": predictions})
-    except Exception as e:
-        return jsonify({"success": False, "error": str(e)})
+# 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)
 
-if __name__ == "__main__":
-    app.run(host="0.0.0.0", port=8000)
+# Launch the interface
+interface.launch(share=True)