Data Mining Assignment - Project using AI Pair Programmer (Github Copilot)
People & Blogs
Introduction
In this article, I'll provide a detailed overview of my recent assignment, which involved creating a Resume Builder application. To enhance efficiency and streamline the coding process, I utilized an AI pair programmer, specifically GitHub Copilot. This application allows users to upload their resumes, receive a score out of 10, and discover specific areas for improvement based on their submissions.
App Functionality Overview
The core functionality of this web application revolves around three major components:
Resume Upload: Users can easily upload their resumes through a user-friendly interface.
Analysis and Scoring: Once the resume is uploaded, the application analyzes the content. It generates a score out of 10 based on various criteria and provides personalized feedback highlighting areas that require improvement.
Feedback Generation: To gather constructive feedback, I formulated a prompt that requests the AI to give three to five specific suggestions for enhancing the resume. Additionally, this prompt requests a score out of 10 to guide users on how well their resume meets specific standards.
Implementation Process
To build this application, I began by instructing GitHub Copilot with the goals I had set. The AI provided a source code that I then refined, addressing a few issues that arose during development. GitHub Copilot also guided me on specific fixes that were needed, which enhanced the overall code quality.
The sequence of operations is as follows:
- The user uploads a PDF version of their resume.
- The application converts the uploaded PDF into text format.
- The text is sent to the Gemini API for analysis, where the prompt formed earlier is executed.
- The response from Gemini, which includes the feedback and score, is parsed and displayed back on the user interface.
Demonstration
To illustrate the functionality, I run the application by uploading a resume. Upon clicking the "Analyze Resume" button, the system processes the document and returns feedback within moments. For instance, I once received a score lower than expected, with suggestions indicating that section headings needed alignment. The feedback provided a detailed explanation for the score, indicating areas for improvement.
The application is designed to provide personalized feedback based on each user’s uploaded resume, ensuring that the advice is relevant to the specific content included.
In conclusion, this project showcases the potential of integrating AI technologies like GitHub Copilot and Gemini to enhance application development and provide valuable user insights.
Keywords
- Resume Builder
- AI Pair Programmer
- GitHub Copilot
- Gemini API
- Resume Analysis
- User Feedback
- Score Generation
- PDF Conversion
FAQ
Q1: What is the primary function of the Resume Builder application?
A1: The primary function is to allow users to upload their resumes, receive a score out of 10, and obtain feedback on how to improve their resumes.
Q2: How does the application analyze resumes?
A2: The application converts uploaded PDFs into text format, sends this text to the Gemini API, which then provides feedback and a scoring based on the content.
Q3: What role does GitHub Copilot play in this project?
A3: GitHub Copilot assists in writing and refining the source code, offering suggestions and solutions to any coding issues encountered during development.
Q4: What kind of feedback does the application provide?
A4: The application provides personalized feedback based on the user's resume, including specific areas that need improvement, along with an explanation of the score received.
Q5: Can the feedback vary for different resumes uploaded?
A5: Yes, the feedback is personalized and varies depending on the specific content of each resume uploaded by the users.