Implement object detection functionality in app.py using Gradio and DiffusionPipeline

app.py

@@ -1,9 +1,33 @@
 import gradio as gr
+from diffusers import DiffusionPipeline
+import torch
 
+# Initialize the pipeline
+pipe = DiffusionPipeline.from_pretrained("Lookingsoft-team/object_detection")
+if torch.cuda.is_available():
+    pipe = pipe.to("cuda")
 
-def greet(name):
-    return "Hello " + name + "!!"
 
+def detect_objects(image):
+    if image is None:
+        return None
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+    # Process the image through the pipeline
+    # Note: This is a placeholder - actual processing will depend on the model's specific requirements
+    results = pipe(image=image)
+
+    # Return the processed image with detections
+    return results.images[0]
+
+
+# Create Gradio interface
+demo = gr.Interface(
+    fn=detect_objects,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Image(type="pil"),
+    title="Object Detection",
+    description="Upload an image and the model will detect objects in it!",
+)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -0,0 +1,5 @@
+gradio>=5.32.1
+torch
+diffusers
+transformers
+accelerate