Update app.py

app.py

@@ -1,14 +1,39 @@
 import gradio as gr
 from transformers import pipeline
+import os
 
 # Load the text summarization pipeline
 summarizer = pipeline("summarization", model="astro21/bart-cls")
 
+chunk_counter = 0
+
 def summarize_text(input_text):
-    # Use the summarization pipeline to generate a summary
-    summarized_text = summarizer(input_text, max_length=128, min_length=64, do_sample=False)
-    return summarized_text[0]['summary_text']
+    global chunk_counter  # Use a global variable to keep track of the chunk number
+    chunk_counter = 0  # Initialize the chunk counter
+
+    # Split the input text into chunks with a maximum size of 512
+    max_chunk_size = 512
+    chunks = [input_text[i:i+max_chunk_size] for i in range(0, len(input_text), max_chunk_size)]
+
+    summarized_chunks = []
+    for chunk in chunks:
+        chunk_counter += 1
+        print(f"Chunk {chunk_counter}:")  # Print the chunk number
+        # Summarize each chunk
+        summarized_chunk = summarizer(chunk, max_length=128, min_length=64, do_sample=False)[0]['summary_text']
+        summarized_chunks.append(summarized_chunk)
+
+    # Concatenate the summaries
+    summarized_text = "\n".join(summarized_chunks)
+    return summarized_text
+
+def summarize_text_file(file):
+    if file is not None:
+        content = file.read().decode("utf-8")
+        return summarize_text(content)
+
+input_type = gr.inputs.File("text")
 
-demo = gr.Interface(fn= summarize_text , inputs="text", outputs="text")
+demo = gr.Interface(fn=summarize_text_file, inputs=input_type, outputs="text", live=True)
 
-demo.launch()
+demo.launch()