Update app.py

app.py

@@ -1,51 +1,26 @@
 from transformers import DetrImageProcessor, DetrForObjectDetection
 import torch
-from PIL import Image, ImageDraw, ImageFont  # Import ImageFont
-import gradio as gr
+from PIL import Image
 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")
+url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+image = Image.open(requests.get(url, stream=True).raw)
 
-    inputs = processor(images=image, return_tensors="pt")
-    outputs = model(**inputs)
+# you can specify the revision tag if you don't want the timm dependency
+processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-101", revision="no_timm")
+model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-101", revision="no_timm")
 
-    # 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]
+inputs = processor(images=image, return_tensors="pt")
+outputs = model(**inputs)
 
-    # Draw bounding boxes and labels on the image
-    draw = ImageDraw.Draw(image)
-    detected_objects = []
-    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)
-        label_text = f"{model.config.id2label[label.item()]}: {round(score.item(), 2)}"
-        # Larger and bolder font
-        draw.text((box[0], box[1]), label_text, fill=color,)
-        detected_objects.append(model.config.id2label[label.item()])
+# 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]
 
-    return image, ', '.join(detected_objects)
-
-
-def upload_image(file):
-    image = Image.open(file.name)
-    image_with_boxes, detected_objects = detect_objects(image)
-    return image_with_boxes, detected_objects
-
-iface = gr.Interface(
-    fn=upload_image,
-    inputs="file",
-    outputs=["image", "text"],
-    title="Object Detection",
-    description="Upload an image and detect objects using DETR model.",
-    allow_flagging=False,
-    css="style.css"  # Path to your custom CSS file
-)
-
-iface.launch()
+for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
+    box = [round(i, 2) for i in box.tolist()]
+    print(
+            f"Detected {model.config.id2label[label.item()]} with confidence "
+            f"{round(score.item(), 3)} at location {box}"
+    )