Upload app.py

app.py

@@ -0,0 +1,126 @@
+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()