Create app.py

app.py

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+import torch
+import gradio as gr
+from PIL import Image
+
+# Load the YOLOv5 model (adjust based on the model you uploaded)
+model = torch.hub.load('ultralytics/yolov5', 'yolov5s')  # You can choose yolov5s, yolov5m, yolov5l
+
+# Function to handle image inference
+def predict_image(image):
+    results = model(image)  # Run inference on the input image
+    results.show()  # Optionally visualize the results (bounding boxes)
+    return results.pandas().xywh  # Return the results (bounding box coordinates)
+
+# Set up Gradio interface to upload an image and get predictions
+interface = gr.Interface(fn=predict_image, inputs=gr.Image(), outputs=gr.Dataframe())
+
+# Launch the interface
+interface.launch()