Fix: Gradio Progress and launch parameters for HF deployment

app.py

@@ -69,7 +69,7 @@ class VoiceModelTrainer:
         learning_rate: float,
         algorithm: str,
         batch_size: int,
-        progress=gr.Progress()
+        progress=None
     ) -> Tuple[str, str, str]:
         """Train the model with RL."""
         if self.training_active:
@@ -77,7 +77,8 @@ class VoiceModelTrainer:
 
         try:
             self.training_active = True
-            progress(0, desc="Initializing training...")
+            if progress:
+                progress(0, desc="Initializing training...")
 
             # Create output directory
             timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
@@ -85,18 +86,21 @@ class VoiceModelTrainer:
             run_dir.mkdir(parents=True, exist_ok=True)
 
             # Load model
-            progress(0.1, desc="Loading model...")
+            if progress:
+                progress(0.1, desc="Loading model...")
             model = VoiceModelWrapper(model_name=model_name, device=self.device)
             model.load_model()
 
             # Setup data (use sample data for demo)
-            progress(0.2, desc="Preparing data...")
+            if progress:
+                progress(0.2, desc="Preparing data...")
             data_manager = DataManager()
             # For HF Space, we'll use a small demo dataset
             # In production, this would load from user-provided data
 
             # Create algorithm
-            progress(0.3, desc=f"Initializing {algorithm.upper()} algorithm...")
+            if progress:
+                progress(0.3, desc=f"Initializing {algorithm.upper()} algorithm...")
             rl_model = model.get_rl_model() if hasattr(model, 'get_rl_model') else model.model
 
             if algorithm.lower() == 'ppo':
@@ -122,7 +126,8 @@ class VoiceModelTrainer:
             metrics_tracker = MetricsTracker(log_dir=str(run_dir / 'logs'))
             visualizer = Visualizer(output_dir=str(run_dir / 'visualizations'))
 
-            progress(0.4, desc="Starting training...")
+            if progress:
+                progress(0.4, desc="Starting training...")
 
             # For demo purposes, simulate training
             # In production, you'd run actual training here
@@ -144,8 +149,9 @@ class VoiceModelTrainer:
 
             # Simulate training progress
             for i in range(num_episodes):
-                progress((0.4 + (i / num_episodes) * 0.5),
-                        desc=f"Training episode {i+1}/{num_episodes}")
+                if progress:
+                    progress((0.4 + (i / num_episodes) * 0.5),
+                            desc=f"Training episode {i+1}/{num_episodes}")
 
             # Save checkpoint
             checkpoint_dir = run_dir / 'checkpoints'
@@ -158,7 +164,8 @@ class VoiceModelTrainer:
                 'episode': num_episodes
             }, checkpoint_path)
 
-            progress(1.0, desc="Training complete!")
+            if progress:
+                progress(1.0, desc="Training complete!")
 
             self.models['trained'] = model
 
@@ -182,14 +189,15 @@ class VoiceModelTrainer:
         self,
         checkpoint_path: str,
         sample_audio: str,
-        progress=gr.Progress()
+        progress=None
     ) -> Tuple[str, str, str]:
         """Generate audio comparison."""
         try:
             if not checkpoint_path or not Path(checkpoint_path).exists():
                 return None, None, "❌ No checkpoint available"
 
-            progress(0, desc="Loading models...")
+            if progress:
+                progress(0, desc="Loading models...")
 
             # For demo, return the input audio
             # In production, process through models
@@ -445,8 +453,4 @@ def create_app():
 
 if __name__ == "__main__":
     app = create_app()
-    app.launch(
-        server_name="0.0.0.0",
-        server_port=7860,
-        share=False
-    )
+    app.launch()