diff --git a/webui.py b/webui.py
index c74146a..22fdfb0 100644
--- a/webui.py
+++ b/webui.py
@@ -1,8 +1,9 @@
 import gradio as gr
 
 from summarizer import load_document, setup_summarization_chain
-from yt_summarizer import summarize_video, check_link
 from translator import setup_translator_chain
+from yt_summarizer import check_link, summarize_video
+
 
 def summarize(url):
     if check_link(url):
@@ -14,11 +15,13 @@ def summarize(url):
 
     return [result, gr.Button("🇹🇷 Translate ", visible=True)]
 
+
 def translate(text):
     llm_chain = setup_translator_chain()
     result = llm_chain.run(text)
     return result
 
+
 with gr.Blocks() as demo:
     gr.Markdown(
         """# Cobanov Web and Video Summarizer
@@ -54,4 +57,4 @@ with gr.Blocks() as demo:
     btn_generate.click(summarize, inputs=[url], outputs=[summary, btn_translate])
     btn_translate.click(translate, inputs=[summary], outputs=[summary])
 
-demo.launch()
\ No newline at end of file
+demo.launch()
diff --git a/yt_summarizer.py b/yt_summarizer.py
index 356069c..8e79620 100644
--- a/yt_summarizer.py
+++ b/yt_summarizer.py
@@ -1,9 +1,10 @@
-from langchain_community.document_loaders import YoutubeLoader
+import re
+
+from langchain.chains.summarize import load_summarize_chain
 from langchain.text_splitter import TokenTextSplitter
 from langchain_community.chat_models import ChatOllama
-from langchain.chains.summarize import load_summarize_chain
+from langchain_community.document_loaders import YoutubeLoader
 from langchain_core.prompts import PromptTemplate
-import re
 
 
 def check_link(link):
@@ -23,9 +24,10 @@ def get_transcript(video_link):
 
 
 def split_chunks(transcript):
-    # Split the transcript into chunks
-    # Llama 3 model takes up to 8192 input tokens, so I set chunk size to 7500 for leaving some space to model.
-    splitter = TokenTextSplitter(chunk_size=7500, chunk_overlap=100)
+
+    splitter = TokenTextSplitter(
+        chunk_size=7500, chunk_overlap=100
+    )  # Llama 3 model takes up to 8192 input tokens, so I set chunk size to 7500 for leaving some space to model.
     chunks = splitter.split_documents(transcript)
     return chunks