Create app.py

app.py

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+import gradio as gr
+import cv2
+from ultralytics import YOLO
+
+# Load the pretrained YOLOv5 model
+yolo_model = YOLO("yolov5s.pt")  # Load the small version of YOLOv5
+
+# Function to perform object detection with a configurable confidence threshold
+def detect_objects(frame, confidence_threshold=0.5):
+    # Convert the frame from BGR (OpenCV) to RGB
+    image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+    
+    # Perform inference with YOLO, passing the confidence threshold
+    results = yolo_model(image, conf=confidence_threshold)  # Set confidence threshold
+    
+    # Draw bounding boxes and labels on the image
+    annotated_image = results.plot()  # This automatically draws boxes and labels
+    
+    # Convert the image back to BGR for displaying in Gradio
+    annotated_image = cv2.cvtColor(annotated_image, cv2.COLOR_RGB2BGR)
+    
+    return annotated_image
+
+# Gradio interface to use the webcam for real-time object detection
+# Added a slider for the confidence threshold
+iface = gr.Interface(fn=detect_objects, 
+                     inputs=[
+                         gr.Video(source="webcam", type="numpy"),  # Webcam input
+                         gr.Slider(minimum=0.0, maximum=1.0, default=0.5, label="Confidence Threshold")  # Confidence slider
+                     ],  
+                     outputs="image")  # Show output image with bounding boxes
+
+iface.launch()