Update app.py

app.py

@@ -276,27 +276,54 @@ Output: {result.stdout if result.stdout else 'Processing completed'}
             
             process.wait()
             
-            progress(0.9, desc="Finalizing model...")
+            progress(0.9, desc="Searching for model files...")
             
-            # Find the generated model file
-            weights_dir = log_dir / "weights"
-            model_files = list(weights_dir.glob("*.pth")) if weights_dir.exists() else []
+            # Search for model files in multiple possible locations
+            possible_paths = [
+                log_dir / "weights",
+                log_dir,
+                self.rvc_dir / "weights" / model_name,
+                self.rvc_dir / "logs" / model_name,
+                self.rvc_dir / "trained_models" / model_name,
+            ]
             
-            if model_files:
-                latest_model = max(model_files, key=lambda p: p.stat().st_mtime)
-                
-                # Copy to output
+            model_files = []
+            for path in possible_paths:
+                if path.exists():
+                    model_files.extend(list(path.glob("**/*.pth")))
+            
+            # Also search for index files
+            index_files = []
+            for path in possible_paths:
+                if path.exists():
+                    index_files.extend(list(path.glob("**/*.index")))
+            
+            if model_files or index_files:
+                # Create output directory
                 output_dir = self.workspace / model_name
                 output_dir.mkdir(exist_ok=True)
-                shutil.copy2(latest_model, output_dir / f"{model_name}.pth")
                 
-                # Copy index if exists
-                index_files = list(log_dir.glob("*.index"))
+                # Copy model file
+                if model_files:
+                    latest_model = max(model_files, key=lambda p: p.stat().st_mtime)
+                    shutil.copy2(latest_model, output_dir / f"{model_name}.pth")
+                    model_size = latest_model.stat().st_size / (1024*1024)  # MB
+                else:
+                    model_size = 0
+                
+                # Copy index file
                 if index_files:
-                    shutil.copy2(index_files[0], output_dir)
+                    latest_index = max(index_files, key=lambda p: p.stat().st_mtime)
+                    shutil.copy2(latest_index, output_dir / latest_index.name)
                 
                 progress(1.0, desc="Training complete!")
                 
+                files_info = []
+                if model_files:
+                    files_info.append(f"- {model_name}.pth ({model_size:.1f}MB)")
+                if index_files:
+                    files_info.append(f"- {latest_index.name}")
+                
                 return f"""✅ Training Complete!
 
 🎓 Model: {model_name}
@@ -304,14 +331,35 @@ Output: {result.stdout if result.stdout else 'Processing completed'}
 ⚙️ Batch Size: {batch_size}
 🎵 Sample Rate: {sample_rate}Hz
 
-💾 Model Files:
-- {output_dir / f'{model_name}.pth'}
-- Index file (if generated)
+💾 Model Files Found:
+{chr(10).join(files_info) if files_info else '- No files found'}
 
-🎉 Ready to download and use!
+📂 Location: {output_dir}
+
+🎉 Ready to download!
 """
             else:
-                return "⚠️ Training completed but model file not found. Check logs directory."
+                # List what's in the log directory for debugging
+                debug_info = []
+                if log_dir.exists():
+                    debug_info.append(f"Log directory exists: {log_dir}")
+                    debug_info.append("Contents:")
+                    for item in log_dir.rglob("*"):
+                        debug_info.append(f"  - {item.relative_to(log_dir)}")
+                
+                return f"""⚠️ Training completed but model files not found.
+
+🔍 Searched in:
+{chr(10).join([f'- {p}' for p in possible_paths])}
+
+📋 Debug Info:
+{chr(10).join(debug_info) if debug_info else 'Log directory not found'}
+
+💡 Possible issues:
+- Training may have failed silently
+- Model files saved to unexpected location
+- Check the RVC logs directory manually
+"""
                 
         except Exception as e:
             return f"❌ Training failed: {str(e)}\n\nNote: Real RVC training requires significant resources. Consider using Google Colab with GPU."