Update app.py

app.py

@@ -0,0 +1,39 @@
+import streamlit as st
+from transformers import AutoImageProcessor, DetaForObjectDetection
+from PIL import Image
+import requests
+
+st.title("Object Detection")
+
+# Sidebar instructions
+st.sidebar.header("Instructions")
+st.sidebar.write("1. Enter an image URL in the text input below.")
+st.sidebar.write("2. Click the 'Detect Objects' button to process the image.")
+
+# Image URL input
+image_url = st.text_input("Enter image URL:", "http://images.cocodataset.org/val2017/000000039769.jpg")
+
+if st.button("Detect Objects"):
+    try:
+        # Load the image
+        image = Image.open(requests.get(image_url, stream=True).raw)
+
+        # Initialize the image processor and model
+        image_processor = AutoImageProcessor.from_pretrained("jozhang97/deta-swin-large")
+        model = DetaForObjectDetection.from_pretrained("jozhang97/deta-swin-large")
+
+        # Process the image
+        inputs = image_processor(images=image, return_tensors="pt")
+        outputs = model(**inputs)
+
+        # Convert outputs to Pascal VOC format
+        target_sizes = torch.tensor([image.size[::-1]])
+        results = image_processor.post_process_object_detection(outputs, threshold=0.5, target_sizes=target_sizes)[0]
+
+        # Display the image and detected objects
+        st.image(image, use_column_width=True)
+        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 {round(score.item(), 3)} at location {box}")
+    except:
+        st.write("Error: Unable to process the image. Please check the URL and try again.")