update app.py

app.py

@@ -1,45 +1,36 @@
-import gradio as gr
-import cv2
-import torch
-from ultralytics import YOLO
-import numpy as np
-
-# Load the YOLOv8 model (replace 'best.pt' with the path to your model)
-model = YOLO('best.pt')
-
-# Function to perform object detection using YOLOv8
-def detect_objects(image):
-    # Convert the image to RGB (OpenCV loads images as BGR)
-    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-
-    # Perform inference on the image
-    results = model.predict(image_rgb)
-
-    # Get bounding boxes and class labels from the results
-    annotated_image = results[0].plot()  # YOLOv8 has a plot method that returns an annotated image
-    
-    return annotated_image
-
-# Gradio Interface (take a photo using webcam or upload an image)
-def take_photo_and_detect():
-    # OpenCV function to take a photo using the webcam
-    def capture_image():
-        cap = cv2.VideoCapture(0)
-        ret, frame = cap.read()
-        cap.release()
-        return frame
-
-    # Set up the Gradio interface
-    demo = gr.Interface(
-        fn=detect_objects,                    # The function to perform object detection
-        inputs=gr.Image(source="webcam", tool="editor", label="Take a photo or upload an image"),
-        outputs=gr.Image(label="Detected Objects"),
-        title="YOLOv8 Object Detection",
-        description="Take a photo or upload an image to detect objects using the YOLOv8 model."
-    )
-
-    demo.launch()
-
-# Start the app
-if __name__ == "__main__":
-    take_photo_and_detect()
+import gradio as gr
+import cv2
+import torch
+from ultralytics import YOLO
+import numpy as np
+
+# Load the YOLOv8 model (replace 'best.pt' with the path to your model)
+model = YOLO('best.pt')
+# Function to perform object detection using YOLOv8
+def detect_objects(image):
+    # Convert the image from PIL format (used by Gradio) to a NumPy array
+    image = np.array(image)
+
+    # Convert the image to RGB (OpenCV loads images as BGR)
+    image_rgb = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)  # Convert from RGB (PIL) to BGR (OpenCV)
+
+    # Perform inference on the image
+    results = model.predict(image_rgb)
+
+    # Get bounding boxes and class labels from the results
+    annotated_image = results[0].plot()  # YOLOv8 has a plot method that returns an annotated image
+    
+    return annotated_image
+
+# Gradio Interface (image upload instead of webcam)
+demo = gr.Interface(
+    fn=detect_objects,                    # The function to perform object detection
+    inputs=gr.Image(type="pil", label="Upload an image"),  # Accept a file upload
+    outputs=gr.Image(label="Detected Shelves"),
+    title="Empty Shelf Detection using YOLOv8",
+    description="Upload an image of shelves, and the model will identify empty spaces using YOLOv8. This tool is designed for recognizing empty shelves in retail or warehouse environments to assist with inventory management and restocking decisions."
+)
+
+# Start the app
+if __name__ == "__main__":
+    demo.launch()