Create app.py

app.py

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+import gradio as gr
+import subprocess
+import os
+
+# YOLOv8 detection function
+def detect_objects(image):
+    # Save the uploaded image temporarily
+    input_image_path = "input.jpg"
+    image.save(input_image_path)
+
+    # Define the YOLOv8 command
+    output_dir = "./runs/detect"
+    os.makedirs(output_dir, exist_ok=True)
+
+    command = [
+        "yolo",  # YOLOv8 CLI command
+        "task=detect",  # Specify the task as detection
+        "mode=predict",  # Set mode to predict
+        f"model=best_p6.pt",  # Path to your YOLOv8 model weights
+        f"source={input_image_path}",  # Input image path (uploaded by user)
+        f"project={output_dir}",  # Output directory
+        f"name=result",  # Subfolder name for results
+        "exist_ok=True"  # Allow overwriting existing results
+    ]
+
+    # Run YOLOv8 inference
+    try:
+        subprocess.run(command, check=True)
+    except Exception as e:
+        return f"Error during YOLO inference: {e}"
+
+    # Get the path to the output image
+    output_image_path = os.path.join(output_dir, "result", os.path.basename(input_image_path))
+
+    # Return the output image for display
+    if os.path.exists(output_image_path):
+        return output_image_path
+    else:
+        return "Error: Output image not found."
+
+# Create the Gradio interface
+interface = gr.Interface(
+    fn=detect_objects,  # The function to call when an image is uploaded
+    inputs=gr.Image(type="pil"),  # Accept images as input
+    outputs=gr.Image(type="file"),  # Return an image as output
+    title="YOLOv8 Object Detection",
+    description="Upload an image of floating waste in water, and this app will detect it using YOLOv8."
+)
+
+# Launch the Gradio app
+interface.launch(share=True)  # Use share=True to get a public URL