Update app.py

app.py

@@ -15,27 +15,28 @@ import requests
 st.title("Imag")
 
 url = st.text_area("Put your URL here")
-image = Image.open(requests.get(url, stream=True).raw)
-
-st.image(image)
-
-model = YolosForObjectDetection.from_pretrained('hustvl/yolos-tiny')
-image_processor = YolosImageProcessor.from_pretrained("hustvl/yolos-tiny")
-
-inputs = image_processor(images=image, return_tensors="pt")
-outputs = model(**inputs)
-
-# model predicts bounding boxes and corresponding COCO classes
-logits = outputs.logits
-bboxes = outputs.pred_boxes
-
-
-# print results
-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(
-        f"Detected {model.config.id2label[label.item()]} with confidence "
-        f"{round(score.item(), 3)} at location {box}"
-    )
+if url:
+    image = Image.open(requests.get(url, stream=True).raw)
+
+    st.image(image)
+
+    model = YolosForObjectDetection.from_pretrained('hustvl/yolos-tiny')
+    image_processor = YolosImageProcessor.from_pretrained("hustvl/yolos-tiny")
+    
+    inputs = image_processor(images=image, return_tensors="pt")
+    outputs = model(**inputs)
+    
+    # model predicts bounding boxes and corresponding COCO classes
+    logits = outputs.logits
+    bboxes = outputs.pred_boxes
+    st.image(bboxes)
+
+    # print results
+    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(
+            f"Detected {model.config.id2label[label.item()]} with confidence "
+            f"{round(score.item(), 3)} at location {box}"
+        )