Integrate ML models with model selector dropdown

- Add model selector with 4 options (B4, B7, B9, MediaPipe)

app.py

@@ -2,12 +2,34 @@ import gradio as gr
 import cv2
 import numpy as np
 from model import ASLDetector
+from model_ml import ASLDetectorML
 
-detector = ASLDetector()
+# Global detector cache for lazy loading
+_detector_cache = {}
 
-def detect_asl(image):
-    """Process image and detect ASL gesture."""
-    print(f"[INFO] detect_asl called - image type: {type(image)}, is None: {image is None}")
+
+def get_detector(model_choice):
+    """Get or create detector instance with lazy loading and caching."""
+    global _detector_cache
+
+    # Check if detector is already cached
+    if model_choice in _detector_cache:
+        return _detector_cache[model_choice]
+
+    # Create new detector instance
+    print(f"[INFO] Creating new detector: {model_choice}")
+
+    detector = ASLDetector() if model_choice == "MediaPipe (Rule-based)" else ASLDetectorML(model_name=model_choice)
+
+    # Cache for future use
+    _detector_cache[model_choice] = detector
+
+    return detector
+
+
+def detect_asl(image, model_choice):
+    """Process image and detect ASL gesture using selected model."""
+    print(f"[INFO] detect_asl called - model: {model_choice}, image type: {type(image)}, is None: {image is None}")
 
     if image is None or not isinstance(image, np.ndarray):
         print(f"[WARN] Invalid input - rejecting image")
@@ -23,36 +45,66 @@ def detect_asl(image):
         image = cv2.cvtColor(image, cv2.COLOR_RGBA2RGB)
         print(f"[INFO] Converted RGBA to RGB")
 
-    # Process image
-    annotated_image, letter, confidence = detector.process_frame(image)
-    print(f"[INFO] Detection result - letter: {letter}, confidence: {confidence}")
-
-    # Create result message
-    if letter and letter != "Unknown":
-        result = f"Detected: {letter} (Confidence: {confidence:.2f})"
-    elif letter == "Unknown":
-        result = "Hand detected but gesture not recognized. Try: A, V, B, 1, or W"
-    else:
-        result = "No hand detected. Please show a clear hand gesture."
-
-    print(f"[INFO] Returning result: {result}")
-    return annotated_image, result
-
-
-# Create Gradio interface with tabs
+    try:
+        # Get or create detector (lazy loading)
+        detector = get_detector(model_choice)
+
+        # Process image
+        annotated_image, letter, confidence = detector.process_frame(image)
+        print(f"[INFO] Detection result - letter: {letter}, confidence: {confidence}")
+
+        # Create result message
+        if letter and letter != "Unknown":
+            result = f"Detected: {letter} (Confidence: {confidence:.2f})\nModel: {model_choice}"
+        elif letter == "Unknown":
+            if model_choice == "MediaPipe (Rule-based)":
+                result = "Hand detected but gesture not recognized. Try: A, V, B, 1, or W"
+            else:
+                result = f"Hand detected but gesture not recognized.\nModel: {model_choice}"
+        else:
+            result = "No hand detected. Please show a clear hand gesture."
+
+        print(f"[INFO] Returning result: {result}")
+        return annotated_image, result
+
+    except Exception as e:
+        error_msg = f"Error loading model: {str(e)}\n\nPlease ensure models are uploaded to HuggingFace Hub.\nSee MODEL_SETUP.md for instructions."
+        print(f"[ERROR] {error_msg}")
+        return image, error_msg
+
+
+# Create Gradio interface with tabs for different input methods
 with gr.Blocks(title="ASL Hand Detection System") as demo:
     gr.Markdown("""
     # ASL Hand Detection System
-    American Sign Language hand gesture detection using MediaPipe.
-
-    **Supported Gestures:**
-    - A: Closed fist
-    - V: Peace sign (index and middle fingers extended)
-    - B: All fingers extended, thumb tucked
-    - 1: Index finger only extended
-    - W: Index, middle, and ring fingers extended
+    American Sign Language hand gesture detection using MediaPipe and Deep Learning.
+
+    - **EfficientNetB4**: Balanced performance and speed (recommended)
+    - **EfficientNetB7**: Higher accuracy, slower inference
+    - **EfficientNetB9**: Highest accuracy, slowest inference
+    - **MediaPipe (Rule-based)**: Fast, lightweight fallback (5 gestures only)
+
+    **Supported Gestures (ML Models):** A-Z, del, nothing, space (29 total)
+
+    **MediaPipe Gestures:** A, V, B, 1, W (5 total)
     """)
 
+    # Model selector dropdown
+    with gr.Row():
+        model_selector = gr.Dropdown(
+            choices=[
+                "EfficientNetB4",
+                "EfficientNetB7",
+                "EfficientNetB9",
+                "MediaPipe (Rule-based)"
+            ],
+            value="MediaPipe (Rule-based)",
+            label="Select Model",
+            info="First-time model (EfficientNet Based) loading may take 5-10 seconds"
+        )
+
+    gr.Markdown("**Note:** Switching between ML models (B4/B7/B9) may take 5-10 seconds on first load as the model downloads from HuggingFace Hub. Subsequent uses will be instant.")
+
     with gr.Tabs():
         with gr.Tab("Take a Picture"):
             with gr.Row():
@@ -71,7 +123,7 @@ with gr.Blocks(title="ASL Hand Detection System") as demo:
 
             webcam_btn.click(
                 fn=detect_asl,
-                inputs=webcam_input,
+                inputs=[webcam_input, model_selector],
                 outputs=[webcam_output, webcam_result]
             )
 
@@ -92,7 +144,7 @@ with gr.Blocks(title="ASL Hand Detection System") as demo:
 
             upload_btn.click(
                 fn=detect_asl,
-                inputs=upload_input,
+                inputs=[upload_input, model_selector],
                 outputs=[upload_output, upload_result]
             )
 
@@ -113,13 +165,14 @@ with gr.Blocks(title="ASL Hand Detection System") as demo:
 
             stream_input.stream(
                 fn=detect_asl,
-                inputs=stream_input,
+                inputs=[stream_input, model_selector],
                 outputs=[stream_output, stream_result]
             )
 
 if __name__ == "__main__":
     try:
         print("[INFO] Starting ASL Hand Detection System...")
+        print("[INFO] Note: First-time model loading may take 5-10 seconds")
         demo.launch()
     except KeyboardInterrupt:
         print("\n[INFO] Shutting down gracefully...")