Update app.py

app.py

@@ -1,29 +1,25 @@
-from flask import Flask, render_template, request, jsonify
-import joblib
-import pandas as pd
-import numpy as np
-
-app = Flask(__name__)
-
-model = joblib.load("music_genre_classifier.pkl")
-scaler = joblib.load("scaler.pkl")
-le = joblib.load("label_encoder.pkl")
-
-@app.route('/')
-def home():
-    return render_template('index.html')
-
-@app.route('/predict', methods = ['POST'])
+@app.route('/predict', methods=['POST'])
 def predict():
     data = request.get_json()
     
+    # 1. Create a DataFrame from the 8 features coming from your website
     input_df = pd.DataFrame([data])
     
-    # Scale and Predict
+    # 2. Get the list of all 58 features the scaler expects
+    expected_features = scaler.feature_names_in_
+    
+    # 3. Add the missing features and set them to 0
+    for col in expected_features:
+        if col not in input_df.columns:
+            input_df[col] = 0.0
+            
+    # 4. Reorder columns to match the exact order seen during training
+    input_df = input_df[expected_features]
+    
+    # 5. Now the scale and predict will work!
     scaled_data = scaler.transform(input_df)
     prediction_idx = model.predict(scaled_data)[0]
     
-    # Get probability / confidence
     probs = model.predict_proba(scaled_data)[0]
     confidence = np.max(probs) * 100
     
@@ -32,7 +28,4 @@ def predict():
     return jsonify({
         'prediction': genre,
         'confidence': confidence
-    })
-
-if __name__ == "__main__":
-    app.run(host = "0.0.0.0", port = 7860)
+    })