Upload app.py

app.py

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+# Import necessary libraries
+import streamlit as st
+import pandas as pd
+import transformers
+import torch
+from huggingface_hub import login
+from dotenv import load_dotenv
+import os
+
+# Import data
+music_data = pd.read_csv("Spotify_Youtube.csv")
+
+# Login to HuggingFace Hub
+load_dotenv()
+HUGGINGFACE_API_KEY = os.environ.get("HUGGINGFACE_API_KEY")
+login(HUGGINGFACE_API_KEY)
+
+# Load Meta LLaMA model
+model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model_id,
+    model_kwargs={"torch_dtype": torch.bfloat16},
+    device_map="auto"
+)
+
+# Function to parse user input using Meta LLaMA model
+def parse_user_input(user_input):
+    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
+        },
+    ]
+
+    prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+
+    terminators = [
+        pipeline.tokenizer.eos_token_id,
+        pipeline.tokenizer.convert_tokens_to_ids("")
+    ]
+
+    outputs = pipeline(
+        prompt,
+        max_new_tokens=256,
+        eos_token_id=terminators,
+        do_sample=True,
+        temperature=0.6,
+        top_p=0.9,
+    )
+    return outputs[0]["generated_text"][len(prompt):]
+
+# Function to create a new dataframe from the music dataframe based on valence, number of tracks, tempo, and 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']]
+
+# 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")
+    st.write("Enter your music preferences in a detailed format and receive a personalized playlist based on your mood")
+    user_prompt = st.text_input("Example: 'I want 20 happy songs with high 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 parameters 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:
+                    st.write("Here are your recommended playlist:")
+                    st.table(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")
+    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")