Update app.py

app.py

@@ -1,26 +1,34 @@
+import streamlit as st
 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
+# Load the DETR model and processor
 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)
+st.title("DETR Object Detection with ResNet-50")
+st.write("Upload an image and let the DETR model detect objects in it.")
+
+# File uploader in Streamlit
+uploaded_file = st.file_uploader("Choose an image...", type="jpg")
+
+if uploaded_file is not None:
+    # Load and display the image
+    image = Image.open(uploaded_file)
+    st.image(image, caption='Uploaded Image', use_column_width=True)
+
+    # Process the image and perform object detection
+    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]
+    # Post-process the results to get bounding boxes and labels with confidence > 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(
-            f"Detected {model.config.id2label[label.item()]} with confidence "
-            f"{round(score.item(), 3)} at location {box}"
-    )
+    # Display results
+    st.write("Detected objects:")
+    for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
+        box = [round(i, 2) for i in box.tolist()]
+        st.write(f"{model.config.id2label[label.item()]}: {round(score.item(), 3)} at location {box}")