Delete detr_gradio_app.py

detr_gradio_app.py

@@ -1,49 +0,0 @@
-import torch
-from PIL import Image
-from transformers import DetrImageProcessor, DetrForObjectDetection
-import gradio as gr
-import matplotlib.pyplot as plt
-import matplotlib.patches as patches
-import io
-
-# Load model and processor once
-processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
-model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
-
-def detect_objects(image):
-    # Run DETR model
-    inputs = processor(images=image, return_tensors="pt")
-    outputs = model(**inputs)
-    target_sizes = torch.tensor([image.size[::-1]])
-    results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0]
-
-    # Draw boxes on image
-    fig, ax = plt.subplots(1)
-    ax.imshow(image)
-
-    for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
-        xmin, ymin, xmax, ymax = box.tolist()
-        ax.add_patch(patches.Rectangle(
-            (xmin, ymin), xmax - xmin, ymax - ymin,
-            linewidth=2, edgecolor='red', facecolor='none'))
-        ax.text(xmin, ymin, f"{model.config.id2label[label.item()]}: {round(score.item(), 2)}",
-                bbox=dict(facecolor='yellow', alpha=0.5), fontsize=8)
-
-    # Save output to bytes buffer
-    buf = io.BytesIO()
-    plt.axis("off")
-    plt.savefig(buf, format='png', bbox_inches='tight', pad_inches=0)
-    plt.close(fig)
-    buf.seek(0)
-    return Image.open(buf)
-
-# Create Gradio interface
-interface = gr.Interface(fn=detect_objects,
-                         inputs=gr.Image(type="pil"),
-                         outputs="image",
-                         title="DETR Object Detection",
-                         description="Upload an image to detect objects using Facebook's DETR model.")
-
-# Launch the app locally
-if __name__ == "__main__":
-    interface.launch(share=True)