Email Extraction is a Python project developed for extracting emails from Gmail accounts and saving them as a CSV file. The project is designed to work seamlessly with Gmail accounts and offers a user-friendly Streamlit interface for ease of use.
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Email Extraction Script: The repository includes the core Python script,
Email_Extraction.py, which is responsible for the email extraction process. This script allows you to retrieve email data from your Gmail account efficiently. -
Streamlit Web Interface: The project leverages Streamlit, a popular Python library for creating web applications, to provide an intuitive and interactive web interface. Users can input their email address and app password, select the mailbox (e.g., Inbox, Starred, Important, Sent), and specify a date range.
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Data Analysis: The extracted email data is processed, and key information such as subject, sender, date, and email body is collected. This data is then presented in tabular format for analysis and export.
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Data Export: The project enables users to export the extracted email data as a CSV file, making it easy to further analyze, visualize, or use the data for various purposes.
This project is deployed on Streamlit and can be accessed through the following link: https://emailextraction.streamlit.app/
Wondering how to use this code? Follow these steps:
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Clone or download this repository to your local machine.
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Open the Streamlit web interface by running the
Email_Extraction.pyscript in your Python environment. Make sure you have the required dependencies installed as specified in therequirements.txtfile. -
Enter your Gmail email address and app password. You can select a mailbox, set a date range, and click the "Get Emails" button to initiate the extraction process.
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The extracted email data will be displayed in a tabular format within the Streamlit web application. You can explore, analyze, and download the data as needed.
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Enjoy the efficiency and convenience of email extraction with Email_extraction!
Or you can read my medium article for step-by-step instructions from here
I will implement a spam email detection algorithm to assess the percentage of spam emails among the total number of emails.
This project is open-source and provided under the terms of the included LICENSE file. Feel free to use, modify, and distribute the code in accordance with the license and credits.
If you have any questions, or feedback, or encounter issues while using Email_extraction, please don't hesitate to reach out:
I value your input and look forward to enhancing Email_extraction to meet your data extraction needs effectively.