File size: 4,125 Bytes
53c14b9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 |
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()
|