Update app.py

app.py

@@ -20,51 +20,4 @@ def main():
 
 if __name__ == "__main__":
     main()
-'''
-import streamlit as st
-
-st.success("welcome")  #green box
-st.error("website")   # red box
-st.info("testing")  # blue box
-
-st.markdown("This is streamlit demo")
-
-course = st.selectbox("Select the course", ['Commerce','Science','Arts'])
-
-gender = st.radio("Select Gender ", ["Male", "Female"])
-
-n = st.text_input("enter the number")
-
-m = st.slider("Select value", min_value=1, max_value=100)
-
-ok = st.button("SUBMIT")
-if ok == True:           #user clicked
-   #st.success(course+"  "+gender)
 
-    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
-    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)
-    
-    # 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]
-    
-    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}"
-        )
-'''