brendabor commited on
Commit
e9335b7
1 Parent(s): 966d96a

Update app.py

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Files changed (1) hide show
  1. app.py +1 -18
app.py CHANGED
@@ -7,15 +7,9 @@ import pandas as pd
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  from sklearn.neighbors import NearestNeighbors
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  from sklearn.preprocessing import StandardScaler
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- # Load the LSTM model for emotion prediction
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- emotion_model = load_model('lstm_model.h5')
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-
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  # Load the KNN model
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  knn_model = joblib.load('knn_model.joblib')
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- # Load the tokenizer
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- tokenizer = joblib.load('tokenizer.pkl')
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-
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  # Load the dataset
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  df = pd.read_csv('df1.csv')
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@@ -48,19 +42,8 @@ st.title('KNN Recommender App')
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  # Get song index from user input
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  song_index_to_recommend = st.number_input('Enter song index:', min_value=0, max_value=len(df)-1, value=0)
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- # Get lyrics for emotion prediction
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- lyrics = df.iloc[song_index_to_recommend]['lyrics']
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-
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- # Process the lyrics
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- sequence = tokenizer.texts_to_sequences([lyrics])
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- padded_sequence = pad_sequences(sequence, maxlen=50) # Adjust the maxlen to match the expected input size
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- emotion = emotion_model.predict(padded_sequence).flatten()
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-
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  # Combine emotion and audio features for recommendation
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- combined_features = np.concatenate([emotion, audio_features_scaled_knn[song_index_to_recommend]])
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-
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- # Display the predicted emotion and recommendations
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- st.write(f"Predicted Emotion: {emotion}")
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  if not knn_recs.empty:
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  st.write("KNN Recommendations:")
 
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  from sklearn.neighbors import NearestNeighbors
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  from sklearn.preprocessing import StandardScaler
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  # Load the KNN model
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  knn_model = joblib.load('knn_model.joblib')
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  # Load the dataset
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  df = pd.read_csv('df1.csv')
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  # Get song index from user input
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  song_index_to_recommend = st.number_input('Enter song index:', min_value=0, max_value=len(df)-1, value=0)
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  # Combine emotion and audio features for recommendation
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+ #combined_features = np.concatenate([emotion, audio_features_scaled_knn[song_index_to_recommend]])
 
 
 
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  if not knn_recs.empty:
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  st.write("KNN Recommendations:")