Upload app_gradio.py

app_gradio.py

@@ -0,0 +1,369 @@
+import gradio as gr
+import os
+from predict import predict_healing_music
+import train_model
+import logging
+import tempfile
+import time
+import shutil
+import socket
+import joblib
+
+# Set up logging
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+)
+logger = logging.getLogger(__name__)
+
+def find_free_port(start_port=7860, max_port=7960):
+    """Find a free port in the given range."""
+    for port in range(start_port, max_port + 1):
+        with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
+            try:
+                s.bind(('', port))
+                return port
+            except OSError:
+                continue
+    return None
+
+# Ensure model directory exists
+model_dir = os.path.join(os.path.dirname(__file__), "models")
+os.makedirs(model_dir, exist_ok=True)
+
+# Model file paths
+model_path = os.path.join(model_dir, "model.joblib")
+scaler_path = os.path.join(model_dir, "scaler.joblib")
+
+# Check if model exists
+if not os.path.exists(model_path) or not os.path.exists(scaler_path):
+    print('First run: Training the model...')
+    try:
+        train_model.train_and_evaluate_model()
+        print('Model training completed!')
+    except Exception as e:
+        print(f'Model training failed: {str(e)}')
+        raise e
+
+def process_audio(audio_path):
+    """
+    Process and analyze the audio file
+    """
+    if audio_path is None:
+        return None, None, None, "Please upload an audio file"
+    
+    model_dir = os.path.join(os.path.dirname(__file__), "models")
+    model_path = os.path.join(model_dir, "model.joblib")
+    scaler_path = os.path.join(model_dir, "scaler.joblib")
+
+    try:
+        # Load model and scaler
+        model = joblib.load(model_path)
+        scaler = joblib.load(scaler_path)
+        
+        tmp_file = None
+        try:
+            # Create temporary file
+            suffix = os.path.splitext(audio_path)[1]
+            tmp_file = tempfile.NamedTemporaryFile(delete=False, suffix=suffix)
+            shutil.copy2(audio_path, tmp_file.name)
+            
+            # Make prediction
+            healing_probability = predict_healing_music(tmp_file.name)
+            
+            if healing_probability is not None:
+                # Calculate percentage
+                healing_percentage = healing_probability * 100
+                
+                # Generate description
+                if healing_percentage >= 75:
+                    description = "This music has strong healing properties! ✨"
+                    color = "#15803d"  # Dark green
+                elif healing_percentage >= 50:
+                    description = "This music has moderate healing effects. 🌟"
+                    color = "#0369a1"  # Dark blue
+                else:
+                    description = "This music has limited healing potential. 🎵"
+                    color = "#b91c1c"  # Dark red
+                
+                return f"{healing_percentage:.1f}%", f'<div style="background-color: {color}; color: white; padding: 1rem; border-radius: 8px; text-align: center;">{description}</div>', None, None
+            else:
+                return "Error", "Error analyzing file. Please ensure it's a valid MP3 or WAV file.", None, None
+                
+        except Exception as e:
+            logger.error(f"Error during analysis: {str(e)}")
+            return "Error", f"An unexpected error occurred: {str(e)}", None, None
+        
+        finally:
+            # Clean up temporary file
+            if tmp_file is not None:
+                try:
+                    tmp_file.close()
+                    os.unlink(tmp_file.name)
+                except Exception as e:
+                    logger.error(f"Failed to clean up temporary file: {str(e)}")
+
+    except Exception as e:
+        logger.error(f"Error during model loading: {str(e)}")
+        return "Error", f"An unexpected error occurred: {str(e)}", None, None
+
+def analyze_audio(audio):
+    """Analyze the audio file"""
+    try:
+        if audio is None:
+            return [
+                gr.update(visible=False),  # results
+                gr.update(visible=False),  # analyzing
+                "",                        # healing_index
+                ""                         # result_text
+            ]
+
+        # Show analyzing status first
+        yield [
+            gr.update(visible=False),      # results
+            gr.update(visible=True),       # analyzing
+            "",                            # healing_index
+            ""                            # result_text
+        ]
+
+        # Process audio and get results
+        index, desc, _, _ = process_audio(audio)
+        desc_with_hint = f'{desc}<div style="margin-top: 1rem; color: #9ca3af; font-size: 0.9rem;">To analyze another file, please refresh the page</div>'
+
+        # Return final results
+        yield [
+            gr.update(visible=True),       # results
+            gr.update(visible=False),      # analyzing
+            index,                         # healing_index
+            desc_with_hint                # result_text
+        ]
+
+    except Exception as e:
+        logger.error(f"Error in analyze_audio: {str(e)}")
+        yield [
+            gr.update(visible=True),       # results
+            gr.update(visible=False),      # analyzing
+            "Error",                       # healing_index
+            f"An error occurred: {str(e)}" # result_text
+        ]
+
+# Custom CSS styles
+custom_css = """
+.gradio-container {
+    font-family: 'Inter', -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, sans-serif;
+    max-width: 800px !important;
+    margin: auto;
+    padding: 0 1rem;
+    background-color: #0f1117;
+}
+.container {
+    max-width: 700px;
+    margin: 0 auto;
+    padding-top: 2rem;
+}
+.header {
+    text-align: center;
+    margin-bottom: 1rem;
+    width: 100%;
+    display: flex;
+    justify-content: center;
+    align-items: center;
+}
+.title {
+    font-size: 2.5rem !important;
+    font-weight: 700 !important;
+    color: white !important;
+    margin: 0 !important;
+    line-height: 1.2 !important;
+    text-align: center !important;
+    white-space: nowrap !important;
+}
+.subtitle {
+    font-size: 1.2rem !important;
+    text-align: center;
+    color: #9ca3af !important;
+    margin-top: 0.5rem !important;
+    max-width: 800px;
+    margin-left: auto;
+    margin-right: auto;
+    white-space: nowrap !important;
+}
+.upload-box {
+    background-color: #1f2937;
+    border-radius: 12px;
+    padding: 2rem;
+    margin-bottom: 1rem;
+    border: 2px dashed #374151;
+    transition: all 0.3s ease;
+}
+.upload-box:hover {
+    border-color: #3b82f6;
+    box-shadow: 0 0 10px rgba(59, 130, 246, 0.2);
+}
+.upload-area {
+    display: flex;
+    flex-direction: column;
+    align-items: center;
+    justify-content: center;
+    gap: 1rem;
+    padding: 1.5rem 0;
+}
+.icon-text-container {
+    display: inline-flex;
+    align-items: center;
+    justify-content: center;
+    gap: 0.2rem;
+    white-space: nowrap;
+}
+.upload-icon {
+    color: #9ca3af;
+    font-size: 2rem;
+    line-height: 1;
+    margin-right: 0.1rem;
+}
+.upload-text {
+    color: white;
+    font-size: 1.1rem;
+    font-weight: 500;
+    line-height: 1;
+}
+.upload-hint {
+    color: #6b7280 !important;
+    font-size: 0.85rem !important;
+    margin-top: 0.5rem !important;
+    font-style: italic !important;
+}
+.progress-area {
+    margin: 1rem 0;
+    background-color: #1f2937;
+    border-radius: 12px;
+    padding: 1.5rem;
+    text-align: center;
+}
+.progress-text {
+    color: #60a5fa !important;
+    font-size: 1.2rem !important;
+    font-weight: 500 !important;
+    margin: 0 !important;
+}
+.results-container {
+    background-color: #1f2937;
+    border-radius: 12px;
+    padding: 1.25rem;
+    margin-top: 1rem;
+    animation: fadeIn 0.5s ease;
+}
+.result-title {
+    color: white !important;
+    font-size: 1.25rem !important;
+    font-weight: 600 !important;
+    margin-bottom: 0.5rem !important;
+}
+.healing-index {
+    font-size: 2.5rem !important;
+    font-weight: 700 !important;
+    text-align: center;
+    color: white !important;
+    margin: 0.5rem 0 !important;
+    animation: scaleIn 0.5s ease;
+}
+.result-text {
+    animation: slideIn 0.5s ease;
+}
+@keyframes fadeIn {
+    from { opacity: 0; transform: translateY(20px); }
+    to { opacity: 1; transform: translateY(0); }
+}
+@keyframes scaleIn {
+    from { transform: scale(0.8); opacity: 0; }
+    to { transform: scale(1); opacity: 1; }
+}
+@keyframes slideIn {
+    from { transform: translateY(10px); opacity: 0; }
+    to { transform: translateY(0); opacity: 1; }
+}
+"""
+
+# Create Gradio interface
+with gr.Blocks(
+    title="Healing Music Classifier",
+    css=custom_css,
+    theme=gr.themes.Default()
+) as demo:
+    with gr.Column(elem_classes="container"):
+        with gr.Row(elem_classes="header"):
+            gr.Markdown("&#127925; Healing Music Classifier", elem_classes="title")
+        
+        gr.Markdown(
+            "Upload your music file, and our model will analyze its healing potential!",
+            elem_classes="subtitle"
+        )
+        
+        with gr.Column(elem_classes="upload-box"):
+            with gr.Column(elem_classes="upload-area"):
+                gr.Markdown("&#9729;&#65039; Drop your audio file here", elem_classes="icon-text-container")
+                audio_input = gr.Audio(
+                    label="Audio Input",
+                    sources=["upload"],
+                    type="filepath",
+                    elem_classes="audio-input",
+                    interactive=True,
+                    label_visible=False,
+                    text="Drop audio file here or click to upload"
+                )
+            gr.Markdown("Limit 200MB per file • MP3, WAV", elem_classes="upload-hint")
+        
+        # Add analyzing status
+        with gr.Column(elem_classes="analyzing-status", visible=False) as analyzing:
+            gr.Markdown(
+                """<div style="display: flex; align-items: center; justify-content: center; gap: 0.5rem;">
+                   <div class="loading-spinner"></div>
+                   <span style="color: #60a5fa;">Analyzing your music...</span>
+                   </div>""",
+                elem_classes="analyzing-text"
+            )
+        
+        with gr.Column(elem_classes="results-container", visible=False) as results:
+            gr.Markdown("Analysis Results", elem_classes="result-title")
+            healing_index = gr.Markdown("", elem_classes="healing-index")
+            result_text = gr.Markdown("", elem_classes="result-text")
+
+    # Add loading spinner CSS
+    demo.load(None, None, None, _js="""
+        () => {
+            const style = document.createElement('style');
+            style.textContent = `
+                .loading-spinner {
+                    width: 20px;
+                    height: 20px;
+                    border: 3px solid #60a5fa;
+                    border-top: 3px solid transparent;
+                    border-radius: 50%;
+                    animation: spin 1s linear infinite;
+                }
+                @keyframes spin {
+                    0% { transform: rotate(0deg); }
+                    100% { transform: rotate(360deg); }
+                }
+            `;
+            document.head.appendChild(style);
+        }
+    """)
+
+    # Audio analysis event
+    audio_input.upload(
+        fn=analyze_audio,
+        inputs=[audio_input],
+        outputs=[
+            results,
+            analyzing,
+            healing_index,
+            result_text
+        ],
+        queue=True
+    )
+
+# Launch application
+if __name__ == "__main__":
+    demo.launch()  # 简化启动配置，让Hugging Face处理端口等
+