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import streamlit as st | |
import numpy as np | |
from tensorflow.keras.models import load_model | |
from tensorflow.keras.preprocessing.sequence import pad_sequences | |
import joblib | |
import pandas as pd | |
from sklearn.neighbors import NearestNeighbors | |
from sklearn.preprocessing import StandardScaler | |
# Load the KNN model | |
knn_model = joblib.load('knn_model.joblib') | |
# Load the dataset | |
df = pd.read_csv('df1.csv') | |
# Preprocess for KNN | |
audio_feature_columns = ['danceability', 'energy', 'key', 'loudness', 'mode', 'speechiness', | |
'acousticness', 'instrumentalness', 'liveness', 'valence', 'tempo', | |
'duration_ms', 'time_signature'] | |
audio_features = df[audio_feature_columns] | |
mood_cats = df[['mood_cats']] | |
mood_cats_df = pd.DataFrame(mood_cats) | |
# Normalize audio features for KNN | |
scaler_knn = StandardScaler() | |
audio_features_scaled_knn = scaler_knn.fit_transform(audio_features) | |
audio_features_df_knn = pd.DataFrame(audio_features_scaled_knn, columns=audio_feature_columns) | |
combined_features_knn = pd.concat([mood_cats_df, audio_features_df_knn], axis=1) | |
# Function for KNN-based recommendation | |
def recommend_knn(query_index, n_recommendations=5): | |
distances, indices = knn_model.kneighbors(combined_features_knn.iloc[query_index].values.reshape(1, -1), n_neighbors=n_recommendations) | |
recommended_songs = df.iloc[indices.flatten()].copy() | |
# Convert distances to scores | |
recommended_songs['score'] = 1 / (1 + distances.flatten()) # Inverse of distance | |
return recommended_songs | |
# Set up the title of the app | |
st.title('KNN Recommender App') | |
# Get song index from user input | |
song_index_to_recommend = st.number_input('Enter song index:', min_value=0, max_value=len(df)-1, value=0) | |
# Combine emotion and audio features for recommendation | |
# combined_features = np.concatenate([emotion, audio_features_scaled_knn[song_index_to_recommend]]) | |
# Get KNN recommendations | |
knn_recs = recommend_knn(song_index_to_recommend) | |
# Display KNN recommendations | |
st.write("KNN Recommendations:") | |
if not knn_recs.empty: | |
for index in knn_recs.index: | |
st.write(f"Song Index: {index}, Title: {df.iloc[index]['title']}, Artist: {df.iloc[index]['artist']}, Score: {knn_recs.loc[index, 'score']}") | |
else: | |
st.write("No recommendations found.") |