app.py

import streamlit as st
import joblib
import numpy as np
from predict import extract_features
import os
import tempfile
from huggingface_hub import hf_hub_download
import logging

# Set up logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)

# Page configuration
st.set_page_config(
    page_title="Healing Music Classifier",
    page_icon="🎵",
    layout="centered"
)

@st.cache_resource
def load_model():
    """Load model from Hugging Face Hub"""
    try:
        logger.info("Downloading model from Hugging Face Hub...")
        model_path = hf_hub_download(
            repo_id="404Brain-Not-Found-yeah/healing-music-classifier",
            filename="models/model.joblib"
        )
        scaler_path = hf_hub_download(
            repo_id="404Brain-Not-Found-yeah/healing-music-classifier",
            filename="models/scaler.joblib"
        )
        
        logger.info("Loading model and scaler...")
        return joblib.load(model_path), joblib.load(scaler_path)
    except Exception as e:
        logger.error(f"Error loading model: {str(e)}")
        return None, None

def main():
    st.title("🎵 Healing Music Classifier")
    st.write("""
    Upload your music file, and AI will analyze its healing potential!
    Supports mp3, wav formats.
    """)

    # Add file upload component
    uploaded_file = st.file_uploader("Choose an audio file...", type=['mp3', 'wav'])

    if uploaded_file is not None:
        # Create progress bar
        progress_bar = st.progress(0)
        status_text = st.empty()

        try:
            # Create temporary file
            with tempfile.NamedTemporaryFile(delete=False, suffix=os.path.splitext(uploaded_file.name)[1]) as tmp_file:
                # Write uploaded file content
                tmp_file.write(uploaded_file.getvalue())
                tmp_file_path = tmp_file.name

            # Update status
            status_text.text("Analyzing music...")
            progress_bar.progress(30)

            # Load model
            model, scaler = load_model()
            if model is None or scaler is None:
                st.error("Model loading failed. Please try again later.")
                return

            progress_bar.progress(50)

            # Extract features
            features = extract_features(tmp_file_path)
            if features is None:
                st.error("Failed to extract audio features. Please ensure the file is a valid audio file.")
                return

            progress_bar.progress(70)

            # Predict
            scaled_features = scaler.transform([features])
            healing_probability = model.predict_proba(scaled_features)[0][1]
            progress_bar.progress(90)

            # Display results
            st.subheader("Analysis Results")

            # Create visualization progress bar
            healing_percentage = healing_probability * 100
            st.progress(healing_probability)

            # Display percentage
            st.write(f"Healing Index: {healing_percentage:.1f}%")

            # Provide explanation
            if healing_percentage >= 75:
                st.success("This music has strong healing properties! 🌟")
            elif healing_percentage >= 50:
                st.info("This music has moderate healing effects. ✨")
            else:
                st.warning("This music has limited healing potential. 🎵")

        except Exception as e:
            st.error(f"An unexpected error occurred: {str(e)}")
            logger.exception("Unexpected error")

        finally:
            # Clean up temporary file
            try:
                if 'tmp_file_path' in locals() and os.path.exists(tmp_file_path):
                    os.unlink(tmp_file_path)
            except Exception as e:
                logger.error(f"Failed to clean up temporary file: {str(e)}")

            # Complete progress bar
            progress_bar.progress(100)
            status_text.text("Analysis complete!")

if __name__ == "__main__":
    main()