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import streamlit as st
import torch
import sys
import os
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
# Path Setup: ensure the app can see the modular package
PROJECT_ROOT = os.path.dirname(os.path.abspath(__file__))
sys.path.append(os.path.join(PROJECT_ROOT, 'src'))
from lyricloop.config import MODEL_ID, RANDOM_STATE
from lyricloop.data import build_inference_prompt, format_lyrics
from lyricloop.metrics import execute_generation
from lyricloop.environment import set_seed
# Page configuration
st.set_page_config(page_title="LyricLoop v2.0", page_icon="🎤", layout="wide")
# Cached model loading
@st.cache_resource
def load_studio_engine():
"""Initializes the Gemma-2b engine for Hugging Face Spaces (CPU)."""
set_seed(RANDOM_STATE)
# Retrieve the token from Hugging Face Space Secrets
hf_token = st.secrets["HF_TOKEN"]
# Use the token in the tokenizer and model loading
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, token=hf_token)
tokenizer.pad_token = tokenizer.eos_token
# Free Tier uses CPU (forcing float32 for stability)
device = "cpu"
dtype = torch.float32
# Load base model skeleton
base_model = AutoModelForCausalLM.from_pretrained(
MODEL_ID,
dtype=dtype,
device_map=device,
token=hf_token
)
# Point to the Hugging Face Model Repository for the adapters
adapter_repo = "lxtung95/lyricloop"
model = PeftModel.from_pretrained(
base_model,
adapter_repo,
token=hf_token
)
return model, tokenizer
# Studio Interface
st.title("LyricLoop v2.0")
st.caption("Professional AI Songwriting Framework | Powered by Gemma-2b")
st.markdown("---")
# Sidebar Configuration
st.sidebar.header("Studio Controls")
creativity = st.sidebar.slider("Creativity (Temperature)", 0.5, 1.2, 0.85)
token_limit = st.sidebar.number_input("Max Tokens", 100, 500, 300)
# Main Input Columns
col1, col2 = st.columns([1, 1])
with col1:
st.subheader("Composition Details")
genre = st.selectbox("Target Genre", ["Pop", "Rock", "Hip-hop", "Electronic", "R&B", "Country"])
artist = st.text_input("Artist Aesthetic", placeholder="e.g., Taylor Swift")
title = st.text_input("Song Title", placeholder="Enter your track title...")
generate_btn = st.button("Compose Lyrics", type="primary", use_container_width=True)
with col2:
st.subheader("Output")
# Create a persistent placeholder for the output
output_placeholder = st.empty()
if generate_btn:
with st.spinner("Model is writing..."):
# Load Engine
model, tokenizer = load_studio_engine()
# Build Inference Prompt
prompt = build_inference_prompt(genre, artist, title)
# Generate Lyrics
raw_output = execute_generation(
model, tokenizer, prompt,
max_tokens=token_limit,
temperature=creativity,
do_sample=True
)
# Post-process formatting
clean_lyrics = format_lyrics(raw_output)
# Update the placeholder specifically
output_placeholder.text_area(
"Final Draft",
clean_lyrics,
height=400,
key="lyrics_output" # adding a key helps mobile state persistence
)
st.download_button(
"Export as TXT",
clean_lyrics,
file_name=f"{title}_lyrics.txt"
) |