Add requirements.txt

app.py

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+from transformers import DetrImageProcessor, DetrForObjectDetection
+import torch
+from PIL import Image, ImageDraw
+import gradio as gr
+import requests
+import random
+
+def detect_objects(image):
+    # Load the pre-trained DETR model
+    processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
+    model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
+
+    inputs = processor(images=image, return_tensors="pt")
+    outputs = model(**inputs)
+
+    # convert outputs (bounding boxes and class logits) to COCO API
+    # let's only keep detections with score > 0.9
+    target_sizes = torch.tensor([image.size[::-1]])
+    results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0]
+
+    # Draw bounding boxes and labels on the image
+    draw = ImageDraw.Draw(image)
+    for i, (score, label, box) in enumerate(zip(results["scores"], results["labels"], results["boxes"])):
+        box = [round(i, 2) for i in box.tolist()]
+        color = (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))
+        draw.rectangle(box, outline=color, width=3)
+        draw.text((box[0], box[1]), model.config.id2label[label.item()], fill=color)
+
+    return image
+
+def upload_image(file):
+    image = Image.open(file.name)
+    image_with_boxes = detect_objects(image)
+    return image_with_boxes
+
+iface = gr.Interface(
+    fn=upload_image,
+    inputs="file",
+    outputs="image",
+    title="Object Detection",
+    description="Upload an image and detect objects using DETR model.",
+    allow_flagging=False
+)
+
+iface.launch()

requirements.txt

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+gradio==2.2.14
+torch==1.2.0
+transformers==4.14.3
+Pillow==9.0.1