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| import pandas as pd | |
| from sentence_transformers import SentenceTransformer, util | |
| class SongMatcher: | |
| def __init__(self, songs_data_file, model_name="sentence-transformers/all-mpnet-base-v2"): | |
| """ | |
| Initializes the SongMatcher with the songs data file and the SentenceTransformer model. | |
| :param songs_data_file: Path to the CSV file containing songs data | |
| :param model_name: Name of the SentenceTransformer model | |
| """ | |
| self.songs_df = pd.read_csv(songs_data_file) | |
| self.sim_model = SentenceTransformer(model_name) | |
| def match_songs_with_sentiment(user_sentiment_label, user_sentiment_score, user_input, score_range): | |
| # New: Define inputVector here | |
| inputVector = self.sim_model.encode(user_input) | |
| # Filter songs with the same sentiment label | |
| matched_songs = self.songs_df[self.songs_df['sentiment'] == user_sentiment_label] | |
| # Calculate the score range | |
| score_min = max(0, user_sentiment_score - score_range) | |
| score_max = min(1, user_sentiment_score + score_range) | |
| # Further filter songs whose scores fall within the specified range | |
| matched_songs = matched_songs[(matched_songs['score'] >= score_min) & (matched_songs['score'] <= score_max)] | |
| # Shuffle the matched songs to get a random order | |
| matched_songs = matched_songs.sample(frac=1).reset_index(drop=True) | |
| matched_songs['similarity'] = matched_songs['seq'].apply(lambda x: util.pytorch_cos_sim(sim_model.encode(x), inputVector)) | |
| top_5 = matched_songs['similarity'].sort_values(ascending=False).head(5) | |
| # Sort the songs by how close their score is to the user's sentiment score | |
| # matched_songs['score_diff'] = abs(matched_songs['score'] - user_sentiment_score) | |
| # matched_songs = matched_songs.sort_values(by='score_diff') | |
| # Select the top five songs and return | |
| return matched_songs.loc[top_5.index, ['song','artist','seq','similarity','sentiment','score']] | |