Update app.py

app.py

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