sepreate table data cleaning form visualization

pages/{15_📈_Table_Data_Clean.py → 15_📈_Table_Data_Cleaning.py}

@@ -1,7 +1,5 @@
 import streamlit as st
 import pandas as pd
-import seaborn as sns
-import matplotlib.pyplot as plt
 import io
 import base64
 
@@ -10,7 +8,10 @@ st.set_page_config(layout="wide")
 
 # Function for the CSV Visualization App
 def app():
-    st.title('CSV Data Cleaning and Visualization')
+
+    st.markdown(hide_streamlit_style, unsafe_allow_html=True)
+
+    st.title('CSV Data Cleaning')
 
     st.markdown("Upload one or multiple CSV files to preprocess and clean your files quickly and stress free.")
     
@@ -18,7 +19,7 @@ def app():
     
     uploaded_files = st.file_uploader("Choose CSV files", type="csv", accept_multiple_files=True)
 
-    # dataframes = []
+    dataframes = []
 
     if uploaded_files:
         for file in uploaded_files:
@@ -73,30 +74,6 @@ def app():
     else:
         st.warning("Please upload CSV file(s).")
         st.stop()
-    
-    st.markdown("")
-    st.markdown("---")
-    st.markdown("")
-    st.markdown("<p style='text-align: center'><a href='https://github.com/Kaludii'>Github</a> | <a href='https://huggingface.co/Kaludi'>HuggingFace</a></p>", unsafe_allow_html=True)
-
-    
-    # uploaded_file = st.file_uploader("Upload your input CSV file", type=["csv"])
-    # Pandas DataFrame is created from the CSV file
-    # if uploaded_file is not None:
-    #     df = pd.read_csv(uploaded_file)
-    #     st.write(df)  # Display the dataframe on the app
-
-    #     # Create a selectbox for user to choose the column to visualize
-    #     columns = df.columns.tolist()
-    #     selected_column = st.selectbox('Select a column to visualize', columns)
-
-    #     # Using seaborn to create a count plot
-    #     fig, ax = plt.subplots()
-    #     sns.countplot(data=df, x=selected_column, ax=ax)
-    #     plt.xticks(rotation=45)  # Rotate X-axis labels to 45 degrees
-    #     # Show the plot
-    #     st.pyplot(fig)
-
 app()