Update Text Summarizer.py

pages/Text Summarizer.py

@@ -1,31 +1,19 @@
 import streamlit as st
-from transformers import pipeline
+from models.text_sumarization.predict import generate_summary
 
-# Title of the web app
 st.title("Text Summarization Tool")
 
-# Load the summarization model
-@st.cache_resource(show_spinner=True)  # Cache the model loading for faster performance
-def load_summarizer():
-    return pipeline("summarization", model="t5-small")
-
-summarizer = load_summarizer()
-
-# Instructions for users
 st.write("Enter the text you'd like to summarize (minimum 50 words).")
 
-# Create a text area for the user to input text
-user_input = st.text_area("Input Text", height=200)
+user_input = st.text_area("Input Text", height=250)
 
 # A button to initiate the summarization process
 if st.button("Summarize"):
-    if len(user_input.split()) < 50:
-        st.warning("Please enter at least 50 words for summarization.")
-    else:
-        # Show a spinner while the summarization is being processed
-        with st.spinner("Summarizing..."):
-            # Generate the summary
-            summary = summarizer(user_input, max_length=150, min_length=30, do_sample=False)
-            # Display the summarized text
-            st.subheader("Summary:")
-            st.write(summary[0]['summary_text'])
+	if len(user_input.split()) < 50:
+		st.warning("Please enter at least 50 words for summarization.")
+	else:
+		# Show a spinner while the summarization is being processed
+		with st.spinner("Summarizing..."):
+			summary = generate_summary(user_input)  # Call the function from predict.py
+			st.subheader("Summary:")
+			st.code(summary, language="text", wrap_lines=True)