Update app.py

app.py

@@ -1,6 +1,5 @@
 import streamlit as st
 from tensorflow.keras.models import load_model
-from tensorflow.keras.preprocessing.text import Tokenizer
 from tensorflow.keras.preprocessing.sequence import pad_sequences
 import joblib
 import pandas as pd
@@ -8,31 +7,46 @@ import numpy as np
 
 # Load your models
 emotion_model = load_model('lstm_model.h5')
-recommender_model = joblib.load('knn_model.npy')
+recommender_model = joblib.load('knn_model.pkl')  # Assuming it's a .pkl file
 
-#load the dataset
-# df = pd.read_csv('path_to_your_dataframe.csv')
+# Load the tokenizer (if used during training)
+# tokenizer = joblib.load('tokenizer.pkl')  # Update with actual file name
 
+# Load the dataset
+df = pd.read_csv('df1.csv')  # Make sure this is the correct DataFrame
 
-st.title("Emotion-based Song Recommender")
+# Set up the title of the app
+st.title('Emotion and Audio Feature-based Song Recommendation System')
 
-# User input for lyrics
-lyrics = st.text_area("Enter lyrics here:")
+# Input field for lyrics
+st.header('Enter Song Lyrics')
+lyrics = st.text_area("Input the lyrics of the song here:")
 
-if st.button("Recommend Songs"):
-    
-    if lyrics:
-        # Predict emotion
-        # Here, ensure that the input shape and preprocessing of lyrics
-        # match the requirements of your LSTM model
+# Input fields for audio features
+st.header('Enter Audio Features')
+audio_features = []
+for feature_name in df.columns:  # Make sure this matches your DataFrame
+    feature = st.number_input(f"Enter value for {feature_name}:", step=0.01)
+    audio_features.append(feature)
+
+# Predict and Recommend button
+if st.button('Predict Emotion and Recommend Songs'):
+    if lyrics and all(audio_features):
         sequence = tokenizer.texts_to_sequences([lyrics])
         padded_sequence = pad_sequences(sequence, maxlen=128)
-        emotion = emotion_model.predict(padded_sequence)  # Adjust this as per your model's requirement
-
-        # Get song recommendations
-        # The recommend method should be defined as part of your KNN model
-        # or as a separate function that uses the KNN model
-        recommendations = recommender_model.recommend(emotion, ...)
-
-        st.write("Emotion Detected:", emotion)
-        st.write("Recommended Songs:", recommendations)
+        emotion = emotion_model.predict(padded_sequence).flatten()  # Flatten if needed
+
+        # Combine emotion and audio features for recommendation
+        combined_features = np.concatenate([[emotion], audio_features])
+
+        # Generate recommendations using the KNN model
+        distances, indices = recommender_model.kneighbors([combined_features], n_neighbors=5)
+        recommended_songs = df.iloc[indices.flatten()]
+
+        # Display emotion and recommendations
+        st.write("Emotion Detected:", emotion[0])  # Adjust as per your model's output
+        st.header('Recommended Songs')
+        for _, song in recommended_songs.iterrows():
+            st.write(song)  # Adjust based on your dataset
+    else:
+        st.error("Please fill in all the fields.")