# import streamlit as st
# from transformers import pipeline 
# summarizer = pipeline("summarization")
# # pipe=pipeline("sentiment-analysis")
# # col1, col2 = st.columns(2)

# # with col1:
# #     x=st.button("Sentiment Analysis")
# # with col2:    
# #     y=st.button("Text Summarization")

# # if x:
# #     t=st.text_input("Enter the Text")
# #     st.write(pipe(t))
# # if y:             
# t1=st.text_input("Enter the Text for Summarization")
# st.write(summarizer(t1))

from transformers import AutoTokenizer, AutoModel
import streamlit as st

tokenizer = AutoTokenizer.from_pretrained("llmware/industry-bert-insurance-v0.1")

model = AutoModel.from_pretrained("llmware/industry-bert-insurance-v0.1")
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("feature-extraction", model="llmware/industry-bert-insurance-v0.1")
   
t=st.text_input("Enter the Text")
st.write(pipe(t))