Update predict.py

models/text_sumarization/predict.py

@@ -1,31 +1,13 @@
-import streamlit as st
 from transformers import pipeline
+import streamlit as st
 
-# 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)
-
-# 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'])
+	"""Load and cache the text summarization pipeline model."""
+	return pipeline("summarization", model="t5-small")
+
+def generate_summary(text: str) -> str:
+	"""Generate a summary for the given input text."""
+	summarizer = load_summarizer()
+	summary = summarizer(text, max_length=150, min_length=30, do_sample=False)
+	return summary[0]["summary_text"]