Create app.py

app.py

@@ -0,0 +1,61 @@
+import streamlit as st
+from transformers import pipeline
+import pandas as pd
+
+# Load the text summarization pipeline
+summarizer = pipeline("summarization", model="astro21/bart-cls")
+
+chunk_counter = 0
+
+
+def summarize_text(input_text):
+    global chunk_counter
+    chunk_counter = 0
+
+    max_chunk_size = 1024
+    chunks = [input_text[i:i + max_chunk_size] for i in range(0, len(input_text), max_chunk_size)]
+
+    summarized_chunks = []
+    chunk_lengths = []
+    summarized_chunks_only = []
+
+    for chunk in chunks:
+        chunk_counter += 1
+        summarized_chunk = summarizer(chunk, max_length=128, min_length=64, do_sample=False)[0]['summary_text']
+        summarized_chunks.append(f"Chunk {chunk_counter}:\n{summarized_chunk}")
+        summarized_chunks_only.append(summarized_chunk)
+
+        chunk_lengths.append(len(chunk))
+
+    summarized_text = "\n".join(summarized_chunks)
+    summarized_text_only = "\n".join(summarized_chunks_only)
+
+    # Save the merged summary to a file
+    with open("summarized.txt", "w") as output_file:
+        output_file.write(summarized_text_only)
+
+    chunk_df = pd.DataFrame({'Chunk Number': range(1, chunk_counter + 1), 'Chunk Length': chunk_lengths})
+
+    return summarized_text, chunk_df, "summarized.txt"
+
+
+def summarize_text_file(file):
+    if file is not None:
+        content = file.read()
+        return summarize_text(content)
+
+
+st.title("Text Summarization")
+st.write("Summarize text using BART")
+
+uploaded_file = st.file_uploader("Upload a text file", type=["txt"])
+
+if uploaded_file is not None:
+    result = summarize_text_file(uploaded_file)
+    st.subheader("Summarized Text")
+    st.write(result[0])
+
+    st.subheader("Chunk Information")
+    st.write(result[1])
+
+    st.markdown(get_binary_file_downloader_html(result[2], "Download Summarized Text"), unsafe_allow_html=True)