Update app.py

app.py

@@ -1,12 +1,12 @@
 import gradio as gr
 from transformers import pipeline
+import concurrent.futures
 
 # Load the summarization model pipeline
 summarizer = pipeline("summarization", model="Falconsai/text_summarization")
 
-# Define the function to handle dynamic chunking of text
+# Function to handle dynamic chunking of text and summarization in parallel
 def summarize_text(text):
-    # Calculate dynamic chunk size (for example, we assume a max chunk of 1024 characters)
     max_chunk_size = 1024  # Can be adjusted based on model's token limit (often 1024-2048 tokens)
     
     # Split the text into chunks if it's longer than the max chunk size
@@ -18,9 +18,11 @@ def summarize_text(text):
     else:
         # If the text is small enough, use it as one chunk
         text_chunks = [text]
-    
-    # Summarize each chunk
-    summaries = [summarizer(chunk)[0]['summary_text'] for chunk in text_chunks]
+
+    # Use ThreadPoolExecutor to summarize each chunk in parallel
+    with concurrent.futures.ThreadPoolExecutor() as executor:
+        # Map summarizer function to each chunk in parallel
+        summaries = list(executor.map(lambda chunk: summarizer(chunk)[0]['summary_text'], text_chunks))
     
     # Combine all summaries into one
     full_summary = " ".join(summaries)