Update app.py

app.py

@@ -1,19 +1,31 @@
+# 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
-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")
+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
 
-# 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))
+pipe = pipeline("feature-extraction", model="llmware/industry-bert-insurance-v0.1")
    
-   
+t=st.text_input("Enter the Text")
+st.write(pipe(t))