Upload app.py

app.py

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+# Streamlit application
+
+# Import necessary libraries
+import streamlit as st
+import pandas as pd
+from openai import OpenAI
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+import os
+from dotenv import load_dotenv
+
+# Import data
+music_data = pd.read_csv("Spotify_Youtube.csv")
+
+# Specify api key for OpenAIs API
+
+load_dotenv()
+
+OPENAI_API_KEY=os.environ.get("OPENAI_API_KEY")
+
+client = OpenAI()
+
+# Function that calls OpenAIs API
+def parse_user_input(user_input):
+    response = client.chat.completions.create(
+      model="gpt-3.5-turbo",
+      messages=[
+        {
+          "role": "system",
+          "content": """You will be provided with an input: '{user_input}', and your task is to determine the following:
+          - Valence: a number that is equal to the mood. Positive moods are closer to 1 and negative moods are closer to 0.
+          - Number of songs: the number of songs the user requests.
+          - Tempo: the tempo of the songs.
+          - Danceability: the danceability of the songs.
+
+          Provide this information in the following format with each value separated by a space:
+          'valence number_of_songs tempo danceability'
+          Example: '0.5 20 120 0.8'
+          """
+        },
+        {
+          "role": "user",
+          "content": user_input
+        },
+        {
+          "role": "assistant",
+          "content": "0.5 20, 120, 0.8"
+        }
+      ],
+      temperature=0.5,
+      max_tokens=64,
+      top_p=1
+    )
+    return response.choices[0].message.content
+
+
+# Function create new dataframe from music dataframe based on valence, number of tracks, tempo an danceability
+def get_tracks_by_artist_and_danceability(music_data, valence, num_tracks, tempo, danceability):
+    filtered_tracks = music_data[
+        (music_data['Valence'].between(valence - 0.1, valence + 0.1)) &
+        (music_data['Tempo'].between(tempo - 30, tempo + 30)) &
+        (music_data['Danceability'].between(danceability - 0.2, danceability + 0.2))
+    ]
+    return filtered_tracks.head(num_tracks)[['Track', 'Artist']]
+
+# Function the recommends tracks by using tfidVectoricer and cosine_similarities
+def recommend_tracks(track_names, track_ids, top_k=20):
+    vectorizer = TfidfVectorizer()
+    tfidf_matrix = vectorizer.fit_transform(track_names)
+    similarities = cosine_similarity(tfidf_matrix)
+    avg_similarity = similarities.mean(axis=0)
+    top_indices = avg_similarity.argsort()[-top_k:][::-1]
+    return track_ids.iloc[top_indices]
+
+# Streamlit Application
+
+logo = "music_logo.png"
+
+# Sidebar
+with st.sidebar:
+  st.image(logo, width=100)
+  st.header("Navigation")
+tab_selection = st.sidebar.radio("Go to", ["Music Generator", "Browse Music", "About Us"])
+
+# Music generator page
+if tab_selection == "Music Generator":
+  st.header("Mood Playlist Generator", divider='rainbow')
+  st.write("Enter your music preferences in a detailed format and recieve a personalized playlist based on your mood:")
+  user_prompt = st.text_input("Example: 'I want 20 happy songs with a lot of tempo that i can dance to!'")
+
+  if st.button("Generate Playlist"):
+      try:
+        with st.spinner("Processing your request..."):
+          parsed_input = parse_user_input(user_prompt)
+          #st.write(f"Parsed input: {parsed_input}")
+
+          # Extract valence and number of songs from the parsed input
+          valence, num_tracks, tempo, danceability = parsed_input.split()
+          valence = float(valence)
+          num_tracks = int(num_tracks)
+          tempo = int(tempo)
+          danceability = (float(danceability))
+
+          #st.write(f"Number of tracks: {num_tracks}, Valence: {valence}, Tempo: {tempo}, Danceability: {danceability}")
+
+          tracks = get_tracks_by_artist_and_danceability(music_data, valence, num_tracks, tempo, danceability)
+          #st.write(f"Found {len(tracks)} tracks.")
+
+          if tracks.empty:
+            st.write("No tracks found. Please try a different query.")
+          else:
+            track_names = tracks['Track'].tolist()
+            track_ids = tracks[['Track', 'Artist']]
+            #st.write("Track names:", track_names)
+
+            recommended_tracks = recommend_tracks(track_names, track_ids, top_k=int(num_tracks))
+            st.write("Here are your recommended playlist:")
+            st.table(recommended_tracks)
+            st.button("Add playlist to Spotify")
+      except ValueError:
+        st.write("Error: Unable to parse the input. Please make sure the format is correct.")
+
+# Browse music page
+elif tab_selection == "Browse Music":
+  st.header("Browse Music", divider='rainbow')
+  st.write("Explore the music data used for generating your playlists.")
+  df = pd.read_csv("Spotify_Youtube.csv")
+  st.dataframe(df)
+
+# About us page
+elif tab_selection == "About Us":
+  st.header("About Us", divider='rainbow')
+  st.write("""
+    This App is developed by 4 ambitious university students, whose goals are to create playlists and music experiences which you can emotionally connect with.
+    """)
+  st.image("group_photo.jpg", caption="Our Team", use_column_width=True)
+
+# Add custom CSS
+st.markdown(
+    """
+    <style>
+    .css-18e3th9 {
+        padding-top: 2rem;
+    }
+    .css-1d391kg {
+        padding-top: 1.5rem;
+    }
+    .css-1d2jrlv {
+        padding-bottom: 1rem;
+    }
+    .css-1v3fvcr {
+        padding-top: 1rem;
+        padding-bottom: 1rem;
+    }
+    </style>
+    """,
+    unsafe_allow_html=True
+)