Update app.py

app.py

@@ -1,11 +1,31 @@
 import streamlit as st
 from transformers import pipeline
 
-pipe=pipeline("sentiment-analysis")
-text=st.text_area("enter the text:")
-#x = st.slider('Select a value')
-#st.write(x, 'squared is', x * x)
-
-if text:
-  out=pipe(text)
-  st.json(out)
+#pipe=pipeline("sentiment-analysis")
+#text=st.text_area("enter the text:")
+##x = st.slider('Select a value')
+##st.write(x, 'squared is', x * x)
+
+#if text:
+  #out=pipe(text)
+  #st.json(out)
+  
+from transformers import DetrFeatureExtractor, DetrForObjectDetection
+from PIL import Image
+import requests
+
+url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
+image = Image.open(requests.get(url, stream=True).raw)
+
+feature_extractor = DetrFeatureExtractor.from_pretrained('facebook/detr-resnet-50')
+model = DetrForObjectDetection.from_pretrained('facebook/detr-resnet-50')
+
+inputs = feature_extractor(images=image, return_tensors="pt")
+outputs = model(**inputs)
+
+# model predicts bounding boxes and corresponding COCO classes
+logits = outputs.logits
+bboxes = outputs.pred_boxes
+
+if bboxes:
+  st.json(bboxes)