Update app.py

app.py

@@ -3,8 +3,6 @@ import validators
 import streamlit as st
 from transformers import AutoTokenizer, pipeline
 
-# local modules
-from summarizer import Summarizer
 from utils import (
     clean_text,
     fetch_article_text,
@@ -12,97 +10,110 @@ from utils import (
     read_text_from_file,
 )
 
+ABS_TOKENIZER_NAME = "facebook/bart-large-cnn"
+ABS_MODEL_NAME = "facebook/bart-large-cnn"
+ABS_MIN_LENGTH = 30
+ABS_MAX_LENGTH = 130
+
+
+@st.cache_resource
+def load_tokenizer():
+    return AutoTokenizer.from_pretrained(ABS_TOKENIZER_NAME)
+
+
+@st.cache_resource
+def load_summarizer():
+    return pipeline(
+        "summarization",
+        model=ABS_MODEL_NAME,
+        tokenizer=ABS_TOKENIZER_NAME,
+    )
+
+
+def normalize_input_text(inp_text, uploaded_file):
+    is_url = bool(inp_text and validators.url(inp_text))
+
+    if is_url:
+        _, clean_txt = fetch_article_text(url=inp_text)
+    elif uploaded_file:
+        clean_txt = read_text_from_file(uploaded_file)
+        clean_txt = clean_text(clean_txt)
+    else:
+        clean_txt = clean_text(inp_text)
+
+    return is_url, clean_txt
+
+
 if __name__ == "__main__":
-    # ---------------------------------
-    # Main Application
-    # ---------------------------------
+    st.set_page_config(page_title="Text Summarization Tool", page_icon="📝")
     st.title("Text Summarization Tool 📝")
 
     st.markdown("---")
-    summarize_type = st.sidebar.selectbox(
-        "Summarization Type", options=["Extractive", "Abstractive"]
-    )
     st.markdown(
-        """This app supports two type of summarization:
+        """
+This app creates **abstractive summaries** using a Hugging Face Transformers summarization pipeline.
 
-1. **Extractive Summarization**: The extractive approach involves picking up the most important phrases and lines from the documents. It then combines all the important lines to create the summary. So, in this case, every line and word of the summary actually belongs to the original document which is summarized.
-2. **Abstractive Summarization**: The abstractive approach involves rephrasing the complete document while capturing the complete meaning of the document. This type of summarization provides more human-like summary"""
-    )
-    # ---------------------------
-    # SETUP & Constants
-    nltk.download("punkt")
-    abs_tokenizer_name = "facebook/bart-large-cnn"
-    abs_model_name = "facebook/bart-large-cnn"
-    abs_tokenizer = AutoTokenizer.from_pretrained(abs_tokenizer_name)
-    abs_max_length = 130
-    abs_min_length = 30
-    # ---------------------------
-
-    inp_text = st.text_input("Enter Text or a URL here")
-    st.markdown(
-        "<h3 style='text-align: center;'>OR</h3>",
-        unsafe_allow_html=True,
+- Paste text
+- Enter a URL
+- Or upload a `.txt`, `.pdf`, or `.docx` file
+"""
     )
+
+    nltk.download("punkt", quiet=True)
+
+    abs_tokenizer = load_tokenizer()
+    abs_summarizer = load_summarizer()
+
+    inp_text = st.text_input("Enter text or a URL here")
+    st.markdown("<h3 style='text-align: center;'>OR</h3>", unsafe_allow_html=True)
     uploaded_file = st.file_uploader(
         "Upload a .txt, .pdf, .docx file for summarization"
     )
 
-    is_url = validators.url(inp_text)
-    if is_url:
-        # complete text, chunks to summarize (list of sentences for long docs)
-        text, clean_txt = fetch_article_text(url=inp_text)
-    elif uploaded_file:
-        clean_txt = read_text_from_file(uploaded_file)
-        clean_txt = clean_text(clean_txt)
-    else:
-        clean_txt = clean_text(inp_text)
+    is_url, clean_txt = normalize_input_text(inp_text, uploaded_file)
 
-    # view summarized text (expander)
     with st.expander("View Input Text"):
-        if is_url:
-            st.write(clean_txt[0])
+        if isinstance(clean_txt, list):
+            st.write(" ".join(clean_txt))
         else:
             st.write(clean_txt)
+
     summarize = st.button("Summarize")
 
-    # called on toggle button [summarize]
     if summarize:
-        if summarize_type == "Extractive":
+        if not clean_txt:
+            st.warning("Please enter text, a URL, or upload a file.")
+            st.stop()
+
+        with st.spinner("Creating summary. This might take a few seconds..."):
             if is_url:
-                text_to_summarize = " ".join([txt for txt in clean_txt])
+                text_chunks = clean_txt if isinstance(clean_txt, list) else [clean_txt]
             else:
-                text_to_summarize = clean_txt
-            # extractive summarizer
-
-            with st.spinner(
-                text="Creating extractive summary. This might take a few seconds ..."
-            ):
-                ext_model = Summarizer()
-                summarized_text = ext_model(text_to_summarize)
-
-        elif summarize_type == "Abstractive":
-            with st.spinner(
-                text="Creating abstractive summary. This might take a few seconds ..."
-            ):
-                text_to_summarize = clean_txt
-                abs_summarizer = pipeline(
-                    "summarization", model=abs_model_name, tokenizer=abs_tokenizer_name
-                )
-
-                if is_url is False:
-                    # list of chunks
-                    text_to_summarize = preprocess_text_for_abstractive_summarization(
-                        tokenizer=abs_tokenizer, text=clean_txt
+                if isinstance(clean_txt, list):
+                    text_chunks = clean_txt
+                else:
+                    text_chunks = preprocess_text_for_abstractive_summarization(
+                        tokenizer=abs_tokenizer,
+                        text=clean_txt,
                     )
-                tmp_sum = abs_summarizer(
-                    text_to_summarize,
-                    max_length=len(text_to_summarize),
-                    min_length=abs_min_length,
+
+            if isinstance(text_chunks, str):
+                text_chunks = [text_chunks]
+
+            summaries = []
+            for chunk in text_chunks:
+                if not chunk or not chunk.strip():
+                    continue
+
+                result = abs_summarizer(
+                    chunk,
+                    max_length=ABS_MAX_LENGTH,
+                    min_length=ABS_MIN_LENGTH,
                     do_sample=False,
                 )
+                summaries.append(result[0]["summary_text"])
 
-                summarized_text = " ".join([summ["summary_text"] for summ in tmp_sum])
+            summarized_text = " ".join(summaries)
 
-        # final summarized output
         st.subheader("Summarized text")
         st.info(summarized_text)