Update app.py

app.py

@@ -1,26 +1,35 @@
+import gradio as gr
 from transformers import DetrImageProcessor, DetrForObjectDetection
 import torch
 from PIL import Image
 import requests
 
-url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-image = Image.open(requests.get(url, stream=True).raw)
-
-# you can specify the revision tag if you don't want the timm dependency
+# Charger le modèle
 processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50", revision="no_timm")
 model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50", revision="no_timm")
 
-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]
-
-for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
-    box = [round(i, 2) for i in box.tolist()]
-    print(
+def detect_objects(image_url):
+    image = Image.open(requests.get(image_url, stream=True).raw)
+    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]
+    
+    detections = []
+    for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
+        box = [round(i, 2) for i in box.tolist()]
+        detections.append(
             f"Detected {model.config.id2label[label.item()]} with confidence "
             f"{round(score.item(), 3)} at location {box}"
-    )
+        )
+    return "\n".join(detections)
+
+# Interface Gradio
+iface = gr.Interface(
+    fn=detect_objects,
+    inputs="text",  # Entrée: une URL d'image
+    outputs="text",  # Sortie: liste des détections
+    description="Paste an image URL to detect objects."
+)
+
+iface.launch()