Update app.py

app.py

@@ -1,36 +1,41 @@
-from typing import List, Dict
-import pandas as pd
-import numpy as np
+import gradio as gr
 import joblib
+import numpy as np
 
-scaler = joblib.load("scaler.joblib")
+# 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'),
 }
 
-class Model:
-    def __init__(self):
-        self.scaler = scaler
-        self.models = 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]
+
+    return predictions
 
-    def __call__(self, inputs: List[List[float]]) -> List[Dict[str, float]]:
-        feature_names = [
-            "course overview", "reading file", "abstract materiale", 
-            "concrete material", "visual materials", "self-assessment", 
-            "exercises submit", "quiz submitted", "playing", "paused", 
-            "unstarted", "buffering"
-        ]
-        outputs = []
-        for features in inputs:
-            input_df = pd.DataFrame([features], columns=feature_names)
-            scaled_input = self.scaler.transform(input_df)
-            predictions = {}
-            for target, model in self.models.items():
-                predictions[target] = model.predict(scaled_input)[0]
-            outputs.append(predictions)
-        return outputs
+# 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)
 
-model = Model()
+# Launch the interface
+interface.launch()