from transformers import AutoImageProcessor, AutoBackbone
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)
processor = AutoImageProcessor.from_pretrained("microsoft/swin-tiny-patch4-window7-224")
model = AutoBackbone.from_pretrained("microsoft/swin-tiny-patch4-window7-224", out_indices=(1,))

inputs = processor(image, return_tensors="pt")
outputs = model(**inputs)
feature_maps = outputs.feature_maps

# import streamlit as st
# from transformers import pipeline

# transcriber = pipeline(task="sentiment-analysis")
# text = st.text_input('Label', 'enter some text!')

# if text:
#     out = transcriber(text)
#     st.json(out)

# uploaded_file = st.file_uploader("Choose a CSV file", accept_multiple_files=True)
# bytes_data = uploaded_file.read()
# st.write("filename:", uploaded_file.name)
# st.write(bytes_data)
# st.image(uploaded_file)