Create Static/app.py

Static/app.py

@@ -0,0 +1,61 @@
+```python
+import os
+import torch
+import logging
+from config import CONFIG
+
+logger = logging.getLogger(__name__)
+
+def setup_static_folder():
+    """Ensure static folder and model weights are available."""
+    static_dir = "static"
+    output_dir = os.path.join(static_dir, "output")
+    
+    # Create static and output directories
+    os.makedirs(static_dir, exist_ok=True)
+    os.makedirs(output_dir, exist_ok=True)
+    logger.info(f"Static directory ensured: {static_dir}")
+    logger.info(f"Output directory ensured: {output_dir}")
+
+    # Check for model weights
+    model_path = CONFIG["MODEL_PATH"]
+    fallback_model = CONFIG["FALLBACK_MODEL"]
+    
+    if not os.path.isfile(model_path):
+        logger.warning(f"Custom model {model_path} not found. Falling back to {fallback_model}.")
+        if not os.path.isfile(fallback_model):
+            logger.info(f"Downloading fallback model: {fallback_model}")
+            try:
+                torch.hub.download_url_to_file(
+                    'https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8n.pt',
+                    fallback_model
+                )
+                logger.info(f"Downloaded {fallback_model}")
+            except Exception as e:
+                logger.error(f"Failed to download {fallback_model}: {e}")
+                raise
+    else:
+        logger.info(f"Using custom model: {model_path}")
+
+    return model_path if os.path.isfile(model_path) else fallback_model
+
+# Call this function before loading the model in app.py
+model_path = setup_static_folder()
+```
+
+This code should be inserted in `app.py` before the `load_model()` function, replacing the existing model loading logic. Update the `load_model()` function to use `model_path`:
+
+```python
+def load_model(model_path):
+    try:
+        model = YOLO(model_path).to(device)
+        if device.type == "cuda":
+            model.model.half()
+        logger.info(f"Model loaded: {model_path}, classes: {model.names}")
+        return model
+    except Exception as e:
+        logger.error(f"Failed to load model: {e}")
+        raise
+
+model = load_model(model_path)
+```