Create app.py

app.py

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+import gradio as gr
+import torch
+from PIL import Image
+import requests
+from io import BytesIO
+
+# Load YOLOv5 pre-trained model from Hugging Face
+model = torch.hub.load('ultralytics/yolov5', 'yolov5s')  # You can choose other versions like yolov5m or yolov5l
+
+# Function for object detection
+def detect_objects(input_image):
+    # If the input is a URL, download the image
+    if isinstance(input_image, str):
+        response = requests.get(input_image)
+        img = Image.open(BytesIO(response.content))
+    else:
+        img = Image.fromarray(input_image)
+    
+    # Run YOLOv5 object detection
+    results = model(img)
+    
+    # Render results on image
+    results.render()  # Render boxes on the image
+    
+    # Return image with detections
+    output_image = results.imgs[0]
+    return Image.fromarray(output_image)
+
+# Create Gradio interface
+interface = gr.Interface(
+    fn=detect_objects, 
+    inputs=gr.inputs.Image(type="numpy", label="Upload an image"), 
+    outputs=gr.outputs.Image(type="pil", label="Detected Image"), 
+    title="YOLOv5 Object Detection",
+    description="Upload an image and detect objects using YOLOv5 model. The model can identify objects like people, cars, animals, and more.",
+    theme="huggingface"
+)
+
+# Launch the interface
+interface.launch()