Upload app.py with huggingface_hub

app.py

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+"""
+Gradio app for mmBERT training on HuggingFace Spaces.
+Runs training in background and shows status via web interface.
+"""
+
+import gradio as gr
+import subprocess
+import threading
+import time
+from pathlib import Path
+
+# Training status
+training_status = {
+    'stage': 'Starting...',
+    'progress': 0,
+    'logs': [],
+    'complete': False,
+    'error': None
+}
+
+def run_training():
+    """Run training in background thread."""
+    global training_status
+    
+    training_status['stage'] = 'Starting training...'
+    training_status['logs'].append('Launching train.py...')
+    
+    try:
+        # Run training script and capture output
+        process = subprocess.Popen(
+            ['python', 'train.py'],
+            stdout=subprocess.PIPE,
+            stderr=subprocess.STDOUT,
+            text=True,
+            bufsize=1
+        )
+        
+        # Read output line by line
+        for line in iter(process.stdout.readline, ''):
+            line = line.strip()
+            if line:
+                training_status['logs'].append(line)
+                # Keep last 100 lines
+                if len(training_status['logs']) > 100:
+                    training_status['logs'] = training_status['logs'][-100:]
+                
+                # Update stage based on output
+                if 'Loading data' in line:
+                    training_status['stage'] = 'Loading data...'
+                elif 'Loading model' in line:
+                    training_status['stage'] = 'Loading model...'
+                elif 'Starting training' in line:
+                    training_status['stage'] = 'Training in progress...'
+                elif 'Evaluating' in line:
+                    training_status['stage'] = 'Evaluating...'
+                elif 'Pushing to Hub' in line:
+                    training_status['stage'] = 'Uploading model...'
+                elif 'Done!' in line:
+                    training_status['stage'] = 'Complete!'
+                    training_status['complete'] = True
+                elif 'loss' in line.lower():
+                    training_status['stage'] = f'Training: {line[-80:]}'
+        
+        process.wait()
+        
+        if process.returncode == 0:
+            training_status['stage'] = '✅ Training complete! Model uploaded.'
+            training_status['complete'] = True
+        else:
+            training_status['stage'] = f'❌ Training failed with exit code {process.returncode}'
+            training_status['error'] = f'Exit code: {process.returncode}'
+            
+    except Exception as e:
+        training_status['stage'] = f'❌ Error: {str(e)}'
+        training_status['error'] = str(e)
+        training_status['logs'].append(f'ERROR: {str(e)}')
+
+def get_status():
+    """Get current training status."""
+    logs = '\n'.join(training_status['logs'][-50:])
+    status = f"## Status: {training_status['stage']}\n\n### Recent Logs:\n```\n{logs}\n```"
+    return status
+
+def refresh_status():
+    """Refresh the status display."""
+    return get_status()
+
+# Start training in background thread
+training_thread = threading.Thread(target=run_training, daemon=True)
+training_thread.start()
+
+# Create Gradio interface
+with gr.Blocks(title="FTM Zone Trainer") as demo:
+    gr.Markdown("# 🚀 FTM Email Zone Classifier Training")
+    gr.Markdown("Training mmBERT model on 38,809 email examples...")
+    
+    status_display = gr.Markdown(value=get_status)
+    
+    refresh_btn = gr.Button("🔄 Refresh Status")
+    refresh_btn.click(fn=refresh_status, outputs=status_display)
+    
+    # Auto-refresh every 10 seconds
+    demo.load(fn=refresh_status, outputs=status_display, every=10)
+
+if __name__ == '__main__':
+    demo.launch(server_name="0.0.0.0", server_port=7860)