Update app.py

app.py

@@ -1,3 +1,49 @@
 import gradio as gr
+from PIL import Image
+import numpy as np
+import cv2
+import os
+import random
 
-gr.load("models/dima806/closed_eyes_image_detection").launch()
+# Assuming overlay_image function is defined elsewhere in your code as provided previously
+
+def process_image_with_model(image_path):
+    # Load the Gradio model for closed eyes detection
+    model = gr.load("models/dima806/closed_eyes_image_detection")
+    
+    # Load the user input image
+    image = Image.open(image_path)
+    image = image.rotate(-90, expand=True)
+    image_array = np.array(image)
+    
+    # Predict using the Gradio model (adjust according to the model's input and output format)
+    predictions = model(image_array)
+
+    # Placeholder loop for overlaying images on detected regions (adjust based on actual predictions format)
+    for bbox in predictions:
+        x, y, w, h = bbox
+        random_eye_image_path = os.path.join("result", random.choice(os.listdir("result")))
+        random_eye_image = cv2.imread(random_eye_image_path, cv2.IMREAD_UNCHANGED)  # Load with alpha channel if present
+
+        if random_eye_image is None:
+            print(f"Failed to load image from {random_eye_image_path}")
+            continue
+
+        # Overlay the image and update image_array with the result
+        image_array = overlay_image(image_array, random_eye_image, x, y, w, h, alpha=0.50)
+
+    return Image.fromarray(image_array)
+
+def gr_interface(image):
+    processed_image = process_image_with_model(image)
+    return processed_image
+
+# Setup the Gradio interface
+demo = gr.Interface(fn=gr_interface,
+                    inputs=gr.Image(type="filepath", label="Upload Image"),
+                    outputs="image",
+                    title="Closed Eyes Image Detection",
+                    description="Upload an image and the model will detect closed eyes.")
+
+if __name__ == "__main__":
+    demo.launch()