Create app.py

app.py

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+import gradio as gr
+from transformers import DetrImageProcessor, DetrForObjectDetection
+import torch
+from PIL import Image, ImageDraw
+import requests
+
+# Load model and processor
+model_name = "facebook/detr-resnet-50"
+processor = DetrImageProcessor.from_pretrained(model_name)
+model = DetrForObjectDetection.from_pretrained(model_name)
+
+# Define prediction function
+def detect_objects(image):
+    # Preprocess image
+    inputs = processor(images=image, return_tensors="pt")
+    outputs = model(**inputs)
+
+    # Process results
+    target_sizes = torch.tensor([image.size[::-1]])  # (height, width)
+    results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0]
+
+    # Draw boxes
+    draw = ImageDraw.Draw(image)
+    for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
+        box = [round(i, 2) for i in box.tolist()]
+        draw.rectangle(box, outline="red", width=3)
+        draw.text((box[0], box[1]), f"{model.config.id2label[label.item()]}: {round(score.item(), 2)}", fill="red")
+
+    return image
+
+# Gradio interface
+demo = gr.Interface(
+    fn=detect_objects,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Image(type="pil"),
+    title="Object Detection with Bounding Boxes",
+    description="Upload an image and the AI will detect and label objects with bounding boxes using a Hugging Face model."
+)
+
+demo.launch()