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LLM_inteface.py

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+import streamlit as st
+import re
+import nltk
+nltk.download('punkt')  # Ensure sentence tokenizers are available.
+# Import your summarizer. Replace these paths with your actual model and tokenizer paths.
+from summarizer import Summarizer
+
+def clean_text(text):
+    """
+    Cleans the text by removing extra whitespace.
+    You can expand this function to include additional cleaning as needed.
+    """
+    cleaned = re.sub(r'\s+', ' ', text).strip()
+    return cleaned
+
+# Initialize your summarizer. Make sure to set the correct paths.
+MODEL_PATH = r"server_side_summarize_news"          # Replace with your model path.
+TOKENIZER_PATH = r"server_side_summarize_news"    # Replace with your tokenizer path.
+summarizer = Summarizer(MODEL_PATH, TOKENIZER_PATH)
+
+# Set up the Streamlit UI.
+st.title("Text Summarizer")
+st.write("Enter your text below to generate a summary:")
+
+# Text area for user input.
+input_text = st.text_area("Input Text", height=200)
+
+# When the 'Summarize' button is pressed, process the input.
+if st.button("Summarize"):
+    if not input_text.strip():
+        st.warning("Please enter some text to summarize.")
+    else:
+        # Clean the input text.
+        cleaned_text = clean_text(input_text)
+        try:
+            # Generate the summary using your summarizer.
+            summary = summarizer.iterative_summarization(cleaned_text)
+            st.subheader("Summary")
+            st.write(summary)
+        except Exception as e:
+            st.error(f"An error occurred during summarization: {e}")

requirements.txt

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+streamlit_pills