diff --git a/requirements.txt b/requirements.txt
index a23cb6a..5a05a4c 100644
Binary files a/requirements.txt and b/requirements.txt differ
diff --git a/run.py b/run.py
index 9c9ceb1..0fc71ab 100644
--- a/run.py
+++ b/run.py
@@ -2,4 +2,4 @@ from streamlit.web import bootstrap
 
 real_s_script = 'sp1_playground.py'
 real_w_script = 'wt2_playground.py'
-bootstrap.run(real_s_script, f'run.py {real_s_script}', [], {})
\ No newline at end of file
+bootstrap.run(real_s_script, f'run.py {real_s_script}', [], {})
diff --git a/wt2_playground.py b/wt2_playground.py
index 63757ed..f3b24cf 100644
--- a/wt2_playground.py
+++ b/wt2_playground.py
@@ -1,3 +1,4 @@
+import streamlit as st
 import openai
 import os
 from dotenv import load_dotenv
@@ -21,8 +22,7 @@ def generate_summarizer(
         temperature=float(temperature),
         top_p=float(top_p),
         frequency_penalty=float(frequency_penalty),
-        messages=
-        [
+        messages=[
             {
                 "role": "system",
                 "content": "You are a IELTS examiner.",
@@ -51,8 +51,6 @@ def generate_summarizer(
     return res["choices"][0]["message"]["content"]
 
 
-import streamlit as st
-
 # Set the application title
 st.title("GPT-3.5 IELTS Examiner")
 
@@ -63,7 +61,7 @@ st.title("GPT-3.5 IELTS Examiner")
 question_type = st.selectbox(
     "What is the question type?",
     (
-        "Writing Task 2"
+        "Writing Task 2",
     ),
 )
 
@@ -79,10 +77,13 @@ answer = st.text_area("Enter the answer:", height=100)
 
 # Slider to control the model hyperparameter
 # with col1:
-token = st.slider("Token", min_value=0.0, max_value=2000.0, value=1000.0, step=1.0)
-temp = st.slider("Temperature", min_value=0.0, max_value=1.0, value=0.7, step=0.01)
+token = st.slider("Token", min_value=0.0,
+                  max_value=2000.0, value=1000.0, step=1.0)
+temp = st.slider("Temperature", min_value=0.0,
+                 max_value=1.0, value=0.7, step=0.01)
 top_p = st.slider("Top_p", min_value=0.0, max_value=1.0, value=0.9, step=0.01)
-f_pen = st.slider("Frequency Penalty", min_value=-1.0, max_value=1.0, value=0.5, step=0.01)
+f_pen = st.slider("Frequency Penalty", min_value=-1.0,
+                  max_value=1.0, value=0.5, step=0.01)
 
 # Showing the current parameter used for the model
 # with col2:
@@ -94,4 +95,5 @@ with st.expander("Current Parameter"):
 
 # Creating button for execute the text summarization
 if st.button("Grade"):
-    st.write(generate_summarizer(token, temp, top_p, f_pen, question_type, question, answer))
+    st.write(generate_summarizer(token, temp, top_p,
+             f_pen, question_type, question, answer))