| 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) | |