Update app.py

app.py

@@ -1,15 +1,41 @@
 
 from transformers import pipeline
 import streamlit as st
+import torch
+from PIL import Image
+import requests
 # Load model directly
 from transformers import AutoImageProcessor, AutoModelForObjectDetection
 
 def model():
-    processor = AutoImageProcessor.from_pretrained("facebook/detr-resnet-50")
-    model = AutoModelForObjectDetection.from_pretrained("facebook/detr-resnet-50")
-    pipe = pipeline("object-detection", model=model)
-    output=pipe("dogs1.webp")    
-    return output
+    from transformers import AutoImageProcessor, DetrForObjectDetection
+    
+    
+    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+    image = Image.open(requests.get(url, stream=True).raw)
+    
+    image_processor = AutoImageProcessor.from_pretrained("facebook/detr-resnet-50")
+    model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
+    
+    inputs = image_processor(images=image, return_tensors="pt")
+    outputs = model(**inputs)
+    
+    # convert outputs (bounding boxes and class logits) to Pascal VOC format (xmin, ymin, xmax, ymax)
+    target_sizes = torch.tensor([image.size[::-1]])
+    results = image_processor.post_process_object_detection(outputs, threshold=0.9, target_sizes=target_sizes)[
+        0
+    ]
+    
+    for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
+        box = [round(i, 2) for i in box.tolist()]
+        st.write("Prediction:", result[0]['label'], "| Score:", result[0]['score'])
+        print(
+            f"Detected {model.config.id2label[label.item()]} with confidence "
+            f"{round(score.item(), 3)} at location {box}"
+        )
+        
+     
+   
 
 def main():
     st.title("Hugging Face Model Demo")