streamlit_app.py

streamlit_app.py

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+import streamlit as st
+import spotipy
+import pandas as pd
+from spotipy.oauth2 import SpotifyClientCredentials
+import librosa
+import librosa.display
+import matplotlib.pyplot as plt
+import numpy as np
+import plotly.express as px
+import plotly.graph_objects as go
+import requests
+import io
+
+
+# Spotify API credentials
+client_id = '1138a66b81e2490593ab49b4573a0c5f'
+client_secret = 'c7d18d1f37c44bd891128790a2f1b982'
+
+# Initialize the Spotipy client
+client_credentials_manager = SpotifyClientCredentials(client_id=client_id, client_secret=client_secret)
+sp = spotipy.Spotify(client_credentials_manager=client_credentials_manager)
+
+# Streamlit app
+st.set_page_config(page_title='Spotify Track Analysis', layout="wide")
+
+# Streamlit app
+st.title('Spotify Track Analysis')
+
+
+
+# Create columns for layout
+col1, col2, col3 = st.columns([1, 3, 1])
+
+# Initialize the selected_track variable
+selected_track = None
+
+selected_track_details = None
+
+
+
+# Section for track selection
+with col2:
+    st.header('Track Selection')
+    
+    # Input field for track search
+    search_query = st.text_input('Search for a track')
+
+    if search_query:
+        # Search for tracks based on user input
+        results = sp.search(q=search_query, type='track', limit=10)
+
+        if results and results['tracks']['items']:
+            st.subheader('Search Results')
+            selected_track = st.selectbox('Select a track', [f"{track['name']} by {track['artists'][0]['name']}" for track in results['tracks']['items']])
+
+            if selected_track:
+                selected_track_details = results['tracks']['items'][[f"{track['name']} by {track['artists'][0]['name']}" for track in results['tracks']['items']].index(selected_track)]
+                track_name = selected_track_details['name']
+                artist_name = selected_track_details['artists'][0]['name']
+                album_name = selected_track_details['album']['name']
+                cover_url = selected_track_details['album']['images'][0]['url']
+                track_id = selected_track_details['id']
+
+                # Display the selected track details
+                st.subheader('Selected Track Details')
+                st.write(f"Track: {track_name}")
+                st.write(f"Artist: {artist_name}")
+                st.write(f"Album: {album_name}")
+                st.image(cover_url,width = 200)
+
+# Section for track analysis
+# Section for track analysis
+with col2:
+    st.header('Track Analysis')
+    
+    if selected_track:
+        selected_track_details = results['tracks']['items'][[f"{track['name']} by {track['artists'][0]['name']}" for track in results['tracks']['items']].index(selected_track)]
+        track_uri = f'spotify:track:{selected_track_details["id"]}'
+        
+        
+        
+        # Load the full audio data for the selected track
+        audio_url = sp.track(track_uri)['preview_url']
+
+        
+        st.info("You can preview the first 30 seconds of the song here. Please note that the full song cannot be played due to copyright issues.")
+
+        
+
+        # Display an audio player for the preview URL
+        st.audio(audio_url, format="audio/mp3")
+
+        if audio_url:
+            response = requests.get(audio_url)
+            y, sr = librosa.load(io.BytesIO(response.content))
+
+            # Visualize highs and lows using a spectrogram
+            st.subheader('Highs and Lows')
+            D = np.abs(librosa.stft(y))
+            fig = px.imshow(librosa.amplitude_to_db(D, ref=np.max), aspect='auto')
+            
+            # Adjust the size of the graph and display it using st.plotly_chart
+            st.plotly_chart(fig, use_container_width=True)
+            st.info("The 'Highs and Lows' graph shows the distribution of high and low frequencies over time. Brighter areas indicate higher energy in the frequency bands.")
+
+            # Visualize beat intensity
+            onset_env = librosa.onset.onset_strength(y=y, sr=sr)
+            times = librosa.times_like(onset_env)
+            onset_frames = librosa.onset.onset_detect(onset_envelope=onset_env)
+            st.subheader('Beat Intensity')
+            fig = go.Figure()
+            fig.add_trace(go.Scatter(x=times, y=onset_env, mode='lines', name='Onset strength'))
+            fig.add_vline(x=times[onset_frames], line_color='red', line_dash='dash', name='Onsets')
+            fig.update_layout(xaxis_title='Time (s)', yaxis_title='Onset Strength')
+
+            # Adjust the size of the graph and display it using st.plotly_chart
+            st.plotly_chart(fig, use_container_width=True)
+            st.info("The 'Beat Intensity' graph represents the rhythm and intensity changes in the music. Red vertical lines indicate the onsets of beats in the track.")
+
+            # Visualize energy changes
+            rms = librosa.feature.rms(y=y)
+            times = librosa.times_like(rms)
+            st.subheader('Energy Changes')
+            fig = go.Figure()
+            fig.add_trace(go.Scatter(x=times, y=rms[0], mode='lines', name='RMS Energy'))
+            fig.update_layout(xaxis_title='Time (s)', yaxis_title='RMS Energy')
+
+            # Adjust the size of the graph and display it using st.plotly_chart
+            st.plotly_chart(fig, use_container_width=True)
+            st.info("The 'Energy Changes' graph displays the root mean square energy (RMSE) of the audio. It shows how the energy in the music changes over time.")
+
+
+            # Visualize tempo variations
+            onset_frames = librosa.onset.onset_detect(onset_envelope=onset_env)
+            st.subheader('Tempo Variations')
+            tempo_changes = 60 / np.diff(times[onset_frames])
+            fig = go.Figure()
+            fig.add_trace(go.Scatter(x=times[onset_frames][:-1], y=tempo_changes, mode='lines', name='Tempo (BPM)'))
+            fig.update_layout(xaxis_title='Time (s)', yaxis_title='Tempo (BPM)')
+
+            # Adjust the size of the graph and display it using st.plotly_chart
+            st.plotly_chart(fig, use_container_width=True)
+            st.info("The 'Tempo Variations' graph reveals the changes in tempo (BPM) over time. It provides insights into tempo fluctuations in the track.")
+        else:
+            st.warning("Audio analysis not available for the selected track.")
+
+# Streamlit app
+st.title('Music Recommendation Based on Audio Features')
+
+# Create sliders for adjusting audio features
+st.header('Adjust Audio Features')
+
+acousticness = st.slider('Acousticness', 0.0, 1.0, 0.5, 0.01)
+st.info("Acousticness measures the amount of acoustic sound in the track. Decreasing it may lead to more electronic or non-acoustic recommendations, while increasing it may result in more acoustic recommendations.")
+danceability = st.slider('Danceability', 0.0, 1.0, 0.5, 0.01)
+st.info("Danceability quantifies how suitable the track is for dancing. Higher values represent tracks that are more danceable.")
+energy = st.slider('Energy', 0.0, 1.0, 0.5, 0.01)
+st.info("Energy measures the intensity and activity in the music. Higher values indicate more energetic tracks, while lower values represent calmer tracks.")
+instrumentalness = st.slider('Instrumentalness', 0.0, 1.0, 0.5, 0.01)
+st.info("Instrumentalness assesses the presence of vocals in the track. Lower values suggest the presence of vocals, while higher values indicate instrumental tracks.")
+liveness = st.slider('Liveness', 0.0, 1.0, 0.5, 0.01)
+st.info("Liveness indicates the likelihood of a live audience in the track. Higher values suggest a live performance, while lower values imply a studio recording.")
+speechiness = st.slider('Speechiness', 0.0, 1.0, 0.5, 0.01)
+st.info("Speechiness quantifies the presence of spoken words in the track. Higher values indicate more speech-like audio, while lower values are more musical and instrumental.")
+valence = st.slider('Valence', 0.0, 1.0, 0.5, 0.01)
+st.info("Valence measures the overall positivity of the track. Higher values represent happier and more positive tracks, while lower values indicate sadder or more negative tracks.")
+
+# Button to generate recommendations
+if st.button('Get Recommendations'):
+    # Build the target_audio_features dictionary
+    target_audio_features = {
+        'acousticness': acousticness,
+        'danceability': danceability,
+        'energy': energy,
+        'instrumentalness': instrumentalness,
+        'liveness': liveness,
+        'speechiness': speechiness,
+        'valence': valence
+    }
+
+     # Fetch track recommendations based on the selected audio features
+    recommended_tracks = sp.recommendations(seed_tracks=[track_id], limit=5, target_audio_features=target_audio_features)
+
+    st.subheader('Recommended Tracks:')
+    
+    if selected_track:
+        # Fetch track recommendations based on the selected audio features
+        recommended_tracks = sp.recommendations(seed_tracks=[track_id], limit=5, target_audio_features=target_audio_features)
+
+    # Create columns for the recommended tracks, e.g., 2 tracks per column
+    num_columns = 2
+    num_recommended_tracks = len(recommended_tracks['tracks'])
+    num_rows = (num_recommended_tracks + num_columns - 1) // num_columns
+    
+    for row in range(num_rows):
+        cols = st.columns(num_columns)
+        for col in range(num_columns):
+            idx = row * num_columns + col
+            if idx < num_recommended_tracks:
+                rec_track = recommended_tracks['tracks'][idx]
+                with cols[col]:
+                    st.write(f"Track: {rec_track['name']}")
+                    st.write(f"Artist: {rec_track['artists'][0]['name']}")
+                    st.write(f"Album: {rec_track['album']['name']}")
+                    album_art_url = rec_track['album']['images'][0]['url']
+                    st.image(album_art_url,width=250)
+
+                    # Get the 'preview_url' for the recommended track
+                    recommended_audio_url = rec_track['preview_url']
+
+                    # Display an audio player for the recommended track's preview URL
+                    st.audio(recommended_audio_url, format="audio/mp3")