Create app.py

app.py

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+import gradio as gr
+import cv2
+import torch
+import numpy as np
+import time
+
+# Load the YOLO model on the CPU
+model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)
+
+def move_robot(command):
+    """
+    Simulate sending a command to the robot to move.
+    Replace this with actual communication code to send commands to the robot.
+    """
+    if command == "forward":
+        print("Robot moving forward")
+    elif command == "backward":
+        print("Robot moving backward")
+    elif command == "left":
+        print("Robot turning left")
+    elif command == "right":
+        print("Robot turning right")
+    elif command == "stop":
+        print("Robot stopped")
+    
+    return f"Command sent: {command}"
+
+def process_camera_feed():
+    """Function to process single frame and run object detection"""
+    try:
+        # Initialize camera
+        cap = cv2.VideoCapture(0)
+        
+        # Check if camera opened successfully
+        if not cap.isOpened():
+            print("Error: Could not open camera")
+            return np.zeros((480, 640, 3), dtype=np.uint8)
+        
+        # Read a frame
+        ret, frame = cap.read()
+        
+        if ret:
+            # Run YOLO detection
+            results = model(frame)
+            processed_frame = results.render()[0]
+            # Convert BGR to RGB for display
+            processed_frame = cv2.cvtColor(processed_frame, cv2.COLOR_BGR2RGB)
+        else:
+            processed_frame = np.zeros((480, 640, 3), dtype=np.uint8)
+            
+        # Release the camera
+        cap.release()
+        
+        return processed_frame
+        
+    except Exception as e:
+        print(f"Error processing camera feed: {e}")
+        return np.zeros((480, 640, 3), dtype=np.uint8)
+
+def continuously_update_feed():
+    """Function to continuously update the camera feed"""
+    while True:
+        yield process_camera_feed()
+        time.sleep(0.1)  # 100ms delay between updates
+
+def robot_interface():
+    with gr.Blocks() as interface:
+        with gr.Row():
+            # Left column for controls
+            with gr.Column(scale=1):
+                # Control buttons
+                forward_btn = gr.Button("Forward", variant="primary")
+                backward_btn = gr.Button("Backward", variant="primary")
+                left_btn = gr.Button("Left", variant="primary")
+                right_btn = gr.Button("Right", variant="primary")
+                stop_btn = gr.Button("Stop", variant="secondary")
+                status_output = gr.Textbox(label="Robot Status")
+
+            # Right column for camera feed
+            with gr.Column(scale=2):
+                # Camera feed output
+                camera_output = gr.Image(
+                    label="Robot Camera Feed with Object Detection",
+                    type="numpy",
+                    height=480
+                )
+                
+                # Update button for camera
+                update_btn = gr.Button("Start Camera", variant="primary")
+                
+        # Set up button click events
+        forward_btn.click(fn=move_robot, inputs=[gr.Textbox(value="forward", visible=False)], outputs=status_output)
+        backward_btn.click(fn=move_robot, inputs=[gr.Textbox(value="backward", visible=False)], outputs=status_output)
+        left_btn.click(fn=move_robot, inputs=[gr.Textbox(value="left", visible=False)], outputs=status_output)
+        right_btn.click(fn=move_robot, inputs=[gr.Textbox(value="right", visible=False)], outputs=status_output)
+        stop_btn.click(fn=move_robot, inputs=[gr.Textbox(value="stop", visible=False)], outputs=status_output)
+        
+        # Set up camera update
+        update_btn.click(
+            fn=process_camera_feed,
+            inputs=None,
+            outputs=camera_output
+        )
+
+    return interface
+
+# Launch the Gradio interface
+if __name__ == "__main__":
+    robot_interface().launch(share=True, debug=True)