Update to have cleaning data function

pages/14_📈_Table_Data_Visualization.py

@@ -2,111 +2,101 @@ import streamlit as st
 import pandas as pd
 import seaborn as sns
 import matplotlib.pyplot as plt
+import io
+import base64
+
 
 st.set_page_config(layout="wide")
 
 # Function for the CSV Visualization App
 def app():
-    st.title('CSV Data Visualization')
+    st.title('CSV Data Cleaning and Visualization')
 
+    st.markdown("Upload one or multiple CSV files to preprocess and clean your files quickly and stress free.")
+    
     # File uploader allows user to add their own CSV
-    uploaded_file = st.file_uploader("Upload your input CSV file", type=["csv"])
-
+    
+    uploaded_files = st.file_uploader("Choose CSV files", type="csv", accept_multiple_files=True)
+
+    # dataframes = []
+
+    if uploaded_files:
+        for file in uploaded_files:
+            file.seek(0)
+            df = pd.read_csv(file)
+            dataframes.append(df)
+    
+        if len(dataframes) > 1:
+            merge = st.checkbox("Merge uploaded CSV files")
+    
+            if merge:
+                # Merge options
+                keep_first_header_only = st.selectbox("Keep only the header (first row) of the first file", ["Yes", "No"])
+                remove_duplicate_rows = st.selectbox("Remove duplicate rows", ["No", "Yes"])
+                remove_empty_rows = st.selectbox("Remove empty rows", ["Yes", "No"])
+                end_line = st.selectbox("End line", ["\\n", "\\r\\n"])
+    
+                try:
+                    if keep_first_header_only == "Yes":
+                        for i, df in enumerate(dataframes[1:]):
+                            df.columns = dataframes[0].columns.intersection(df.columns)
+                            dataframes[i+1] = df
+    
+                    merged_df = pd.concat(dataframes, ignore_index=True, join='outer')
+    
+                    if remove_duplicate_rows == "Yes":
+                        merged_df.drop_duplicates(inplace=True)
+    
+                    if remove_empty_rows == "Yes":
+                        merged_df.dropna(how="all", inplace=True)
+    
+                    dataframes = [merged_df]
+    
+                except ValueError as e:
+                    st.error("Please make sure columns match in all files. If you don't want them to match, select 'No' in the first option.")
+                    st.stop()
+    
+        # Show or hide DataFrames
+        show_dataframes = st.checkbox("Show DataFrames", value=True)
+    
+        if show_dataframes:
+            for i, df in enumerate(dataframes):
+                st.write(f"DataFrame {i + 1}")
+                st.dataframe(df)
+    
+        if st.button("Download cleaned data"):
+            for i, df in enumerate(dataframes):
+                csv = df.to_csv(index=False)
+                b64 = base64.b64encode(csv.encode()).decode()
+                href = f'<a href="data:file/csv;base64,{b64}" download="cleaned_data_{i + 1}.csv">Download cleaned_data_{i + 1}.csv</a>'
+                st.markdown(href, unsafe_allow_html=True)
+    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
+    # 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)
+    #     # 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)
+    #     # 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()
 
-# import io
-# import base64
-
-# st.set_page_config(page_title="Data Cleaning Tool")
-
-# hide_streamlit_style = """
-#             <style>
-#             #MainMenu {visibility: hidden;}
-#             footer {visibility: hidden;}
-#             </style>
-#             """
-# st.markdown(hide_streamlit_style, unsafe_allow_html=True)
-
-# st.title("CSV Data Cleaning Tool")
-
-# st.markdown("Upload one or multiple CSV files to preprocess and clean your files quickly and stress free.")
-
-# uploaded_files = st.file_uploader("Choose CSV files", type="csv", accept_multiple_files=True)
-
-# dataframes = []
-
-# if uploaded_files:
-#     for file in uploaded_files:
-#         file.seek(0)
-#         df = pd.read_csv(file)
-#         dataframes.append(df)
-
-#     if len(dataframes) > 1:
-#         merge = st.checkbox("Merge uploaded CSV files")
-
-#         if merge:
-#             # Merge options
-#             keep_first_header_only = st.selectbox("Keep only the header (first row) of the first file", ["Yes", "No"])
-#             remove_duplicate_rows = st.selectbox("Remove duplicate rows", ["No", "Yes"])
-#             remove_empty_rows = st.selectbox("Remove empty rows", ["Yes", "No"])
-#             end_line = st.selectbox("End line", ["\\n", "\\r\\n"])
-
-#             try:
-#                 if keep_first_header_only == "Yes":
-#                     for i, df in enumerate(dataframes[1:]):
-#                         df.columns = dataframes[0].columns.intersection(df.columns)
-#                         dataframes[i+1] = df
-
-#                 merged_df = pd.concat(dataframes, ignore_index=True, join='outer')
-
-#                 if remove_duplicate_rows == "Yes":
-#                     merged_df.drop_duplicates(inplace=True)
-
-#                 if remove_empty_rows == "Yes":
-#                     merged_df.dropna(how="all", inplace=True)
-
-#                 dataframes = [merged_df]
-
-#             except ValueError as e:
-#                 st.error("Please make sure columns match in all files. If you don't want them to match, select 'No' in the first option.")
-#                 st.stop()
-
-#     # Show or hide DataFrames
-#     show_dataframes = st.checkbox("Show DataFrames", value=True)
-
-#     if show_dataframes:
-#         for i, df in enumerate(dataframes):
-#             st.write(f"DataFrame {i + 1}")
-#             st.dataframe(df)
-
-#     if st.button("Download cleaned data"):
-#         for i, df in enumerate(dataframes):
-#             csv = df.to_csv(index=False)
-#             b64 = base64.b64encode(csv.encode()).decode()
-#             href = f'<a href="data:file/csv;base64,{b64}" download="cleaned_data_{i + 1}.csv">Download cleaned_data_{i + 1}.csv</a>'
-#             st.markdown(href, unsafe_allow_html=True)
-# 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)