Update app.py

app.py

@@ -1,73 +1,36 @@
+from typing import List, Dict
+import pandas as pd
 import numpy as np
 import joblib
-import json
-from typing import List, Dict
 
-# Load the scaler and models
-scaler = joblib.load('scaler.joblib')
+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"),
 }
 
-# Define the prediction function
-def predict(features: List[float]) -> Dict[str, float]:
-    """
-    Predict outcomes for all target variables based on input features.
-
-    Args:
-        features (List[float]): A list of 12 numeric features in the correct order.
-
-    Returns:
-        Dict[str, float]: A dictionary with predictions for each target variable.
-    """
-    # Ensure the input is a NumPy array
-    input_array = np.array(features).reshape(1, -1)
-
-    # Scale the input
-    scaled_input = scaler.transform(input_array)
-
-    # Predict outcomes
-    predictions = {}
-    for target, model in models.items():
-        predictions[target] = model.predict(scaled_input)[0]  # Get single prediction
-
-    return predictions
-
-# Define a callable class for Hugging Face
 class Model:
     def __init__(self):
         self.scaler = scaler
         self.models = models
 
     def __call__(self, inputs: List[List[float]]) -> List[Dict[str, float]]:
-        """
-        Hugging Face expects the model to handle a batch of inputs.
-
-        Args:
-            inputs (List[List[float]]): A batch of feature vectors.
-
-        Returns:
-            List[Dict[str, float]]: A list of predictions for each input.
-        """
+        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:
-            predictions = predict(features)
+            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
 
-
-# Instantiate the model
 model = Model()
-
-# Hugging Face Inference API expects `model` to be callable
-if __name__ == "__main__":
-    # For local testing or debugging
-    test_input = [
-        [0.5, 1.0, 0.0, 1.0, 0.5, 0.0, 1.0, 0.5, 1.0, 0.0, 0.0, 0.5]  # Example input
-    ]
-    output = model(test_input)
-    print(json.dumps(output, indent=4))
-