app.py

# To RUN Streamlit locally in browser
#  C:\Users\Maurice\Documents\Python>streamlit run C:\Users\Maurice\Documents\Python\app.py
#      You can now view your Streamlit app in your browser.

#      Local URL: http://localhost:8501
#      Network URL: http://192.168.1.23:8501

# Imports
import streamlit as st
import pandas as pd
import os
from io import BytesIO


#Setup App
st.set_page_config(page_title="Data Sweeper", layout='wide')
st.title("Data Sweeper")
st.write("Transform your files between CSV and Excel formats with built-in data cleaning and visualisation!")

uploaded_files=st.file_uploader("Upload your files(CSV or Excel):", type=["csv","xlsx"],accept_multiple_files=True)

if uploaded_files:
	for file in uploaded_files:
		file_ext = os.path.splitext(file.name)[-1].lower()
		if file_ext == ".csv":
			df = pd.read_csv(file)
		elif file_ext == ".xlsx":
			df = pd.read_excel(file)
		else:
			st.error(f"Unsupported file type: {file_ext}")
			continue
		#Display file information. Name and Size
		st.write(f"**Filename** {file.name}")
		st.write(f"** File Size** {file.size/1024}")
		
		#show 5 rows of our dataframe
		st.write("Preview the Head of the Dataframe.")
		st.dataframe(df.head())
		
		#Options for Data Cleaning
		st.subheader("Data Cleaning Options")
		if st.checkbox(f"Clean Data for {file.name}"):
			col1, col2 = st.columns(2)
			
			with col1:
				if st.button(f"Remove Duplicates from {file.name}"):
					df.drop_duplicates(inplace=True)
					st.write("Duplicates Removed!")
			with col2:
				if st.button(f"Fill Missing Values in {file.name}"):
					numeric_cols = df.select_dtypes(include=['number']).columns
					df[numeric_cols] = df[numeric_cols].fillna(df[numeric_cols].mean())
					st.write("Missing Values have been filled!")
					
		#Choose specific Columns to Keep or Convert
		st.subheader("Select Columns to Convert")
		columns=st.multiselect(f"Choose Columns for {file.name}", df.columns, default=df.columns)
		df = df[columns]
		
		#Create Some Visualisations
		st.subheader("Data Visualisation")
		if st.checkbox(f"Show Visualisation for {file.name}"):
			st.bar_chart(df.select_dtypes(include='number').iloc[:,:2])
			
		#Conversion File Option
		st.subheader("Conversion Options")
		conversion_type=st.radio(f"Convert {file.name} to:",["CSV","Excel"], key=file.name)
		if st.button(f"Convert {file.name}"):
			buffer = BytesIO()
			if conversion_type == "CSV":
				df.to_csv(buffer,index=False)
				file.name=file.name.replace(file_ext,".csv")
				mime_type="text/csv"
					
			if conversion_type == "Excel":
				df.to_excel(buffer,index=False)
				file.name=file.name.replace(file_ext,".xlsx")
				mime_type="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
			buffer.seek(0)
				
			#Download Button
			st.download_button(
				label=f"Download {file.name} as {conversion_type}",
				data=buffer,
				file_name=file.name,
				mime=mime_type
			)
st.success("All files processed")