Upload app.py

app.py

@@ -0,0 +1,38 @@
+import joblib
+import pandas as pd
+import numpy as np
+from sklearn.preprocessing import LabelEncoder
+
+model = joblib.load("mood_prediction_model.pkl")
+label_encoder = joblib.load("mood_label_encoder.pkl")
+
+genre_mapping = {
+    "Hip-Hop": 0, "Pop": 1, "Electronic": 2, "Folk": 3, "Classical": 4,
+    "Jazz": 5, "Country": 6, "R&B": 7, "Reggae": 8
+}
+
+def predict_mood(data):
+    genre = data['genre']
+    popularity = data['popularity']
+    stream = data['stream']
+    duration = data['duration']
+    explicit_content = data['explicit_content']
+
+    genre_encoded = genre_mapping.get(genre, -1)
+    explicit_content_encoded = 1 if explicit_content.lower() == "yes" else 0
+    
+    input_data = pd.DataFrame([{
+        'genre_encoded': genre_encoded,
+        'popularity': popularity,
+        'stream': stream,
+        'duration': duration,
+        'explicit_content_encoded': explicit_content_encoded
+    }])
+
+    mood_encoded = model.predict(input_data)[0]
+    mood = label_encoder.inverse_transform([mood_encoded])[0]
+    
+    return {"predicted_mood": mood}
+
+def predict(inputs):
+    return predict_mood(inputs)