Update song_matching.py

song_matching.py

@@ -11,29 +11,28 @@ class SongMatcher:
         self.songs_df = pd.read_csv(songs_data_file)
         self.sim_model = SentenceTransformer(model_name)
 
-    def match_songs_with_sentiment(self, user_sentiment_label, user_sentiment_score, user_input, score_range=0.00625):
-        """
-        Matches songs from the dataset with the user's sentiment.
-        :param user_sentiment_label: The sentiment label of the user input
-        :param user_sentiment_score: The sentiment score of the user input
-        :param user_input: Text input from the user
-        :param score_range: Range for filtering songs based on sentiment score
-        :return: DataFrame of top 5 matched songs
-        """
+    def match_songs_with_sentiment(user_sentiment_label, user_sentiment_score,inputVector, score_range,songs_df):
+    
         # Filter songs with the same sentiment label
-        matched_songs = self.songs_df[self.songs_df['sentiment'] == user_sentiment_label]
-
+        matched_songs = songs_df[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)]
-
-        # Compute similarity between user input and song lyrics
-        input_vector = self.sim_model.encode(user_input)
-        matched_songs['similarity'] = matched_songs['seq'].apply(lambda x: util.pytorch_cos_sim(self.sim_model.encode(x), input_vector))
-
-        # Select the top five songs based on similarity and return
-        top_5 = matched_songs.nlargest(5, 'similarity')
-        return top_5[['song', 'artist', 'seq', 'similarity', 'sentiment', 'score']]
+    
+        # 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']]