Update app.py

app.py

@@ -2,31 +2,43 @@ import streamlit as st
 from annotated_text import annotated_text
 from io import StringIO
 
-"""
-# Annotated text example
-
-Below is an example of how to use the annotated_text function:
-
-
-with st.echo():
-    annotated_text(
-        "This ",
-        ("is", "verb"),
-        " some ",
-        ("annotated", "adj"),
-        ("text", "noun"),
-        " for those of ",
-        ("you", "pronoun"),
-        " who ",
-        ("like", "verb"),
-        " this sort of ",
-        ("thing", "noun"),
-        "."
-    )
-"""
+import os
+os.environ['KMP_DUPLICATE_LIB_OK']='True'
+
+import plotly.express as px
+from streamlit_option_menu import option_menu
+
+st. set_page_config(layout="wide")
+
+from transformers import pipeline
+import pandas as pd
+
+@st.cache(allow_output_mutation = True)
+def init_text_summarization_model():
+    MODEL = 'facebook/bart-large-cnn'
+    pipe = pipeline("summarization", model=MODEL)
+    return pipe
+
+@st.cache(allow_output_mutation = True)
+def init_zsl_topic_classification():
+    MODEL = 'facebook/bart-large-mnli'
+    pipe = pipeline("zero-shot-classification", model=MODEL)
+    template = "This text is about {}."
+    return pipe, template
+
+# Model initialization    
+pipeline_summarization = init_text_summarization_model()
+pipeline_zsl, template = init_zsl_topic_classification()
+
+st.header("Intelligent Document Automation")
+
 uploaded_file = st.file_uploader("Choose a file")
 
+def get_text_from_ocr_engine(uploaded_file):
+    return "This is a sample text for named entity recognition and other tasks"
+
+
 if uploaded_file is not None:
-    # To convert to a string based IO:
-    stringio = StringIO(uploaded_file.getvalue().decode("utf-8"))
-    st.write(stringio)
+    ocr_text  = get_text_from_ocr_engine(uploaded_file)
+    st.write(ocr_text)
+