Update app.py

app.py

@@ -1,49 +1,3 @@
-"""import gradio as gr
-from ultralytics import YOLO
-from PIL import Image
-import numpy as np
-
-# Load the YOLO model
-MODEL_URL = "https://huggingface.co/ayoubsa/yolo_model/resolve/main/best.pt"
-model = YOLO(MODEL_URL)
-
-# Define the prediction function
-def predict(input_img):
-    try:
-        # Convert PIL Image to NumPy array
-        image_array = np.array(input_img)
-
-        # Perform inference
-        results = model(image_array)
-
-        # Extract detected class names
-        detected_classes = [model.names[int(cls)] for cls in results[0].boxes.cls]
-
-        # Render results on the image
-        results[0].plot()  # Render bounding boxes on the image
-        output_image = Image.fromarray(results[0].orig_img)
-
-        return output_image, {cls: 1.0 for cls in detected_classes}  # Dummy scores for simplicity
-    except Exception as e:
-        print(f"Error during processing: {e}")
-        return None, {"Error": str(e)}
-
-# Gradio app configuration
-gradio_app = gr.Interface(
-    predict,
-    inputs=gr.Image(label="Upload an Image", type="pil"),
-    outputs=[
-        gr.Image(label="Predicted Image with Bounding Boxes"),  # Rendered image with bounding boxes
-        gr.Label(label="Detected Classes"),  # Detected class names
-    ],
-    title="YOLO Object Detection App",
-    description="Upload an image, and the YOLO model will detect objects in it.",
-)
-
-if __name__ == "__main__":
-    gradio_app.launch()"""
-
-
 import gradio as gr
 from ultralytics import YOLO
 from PIL import Image