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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")
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