https://github.com/r4255/htmlresume

https://github.com/r4255/htmlresume

Last synced: 21 days ago
JSON representation

• Host: GitHub
• URL: https://github.com/r4255/htmlresume
• Owner: R4255
• Created: 2024-09-14T19:27:52.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2024-09-14T20:32:15.000Z (4 months ago)
• Last Synced: 2024-11-09T22:39:48.454Z (3 months ago)
• Language: Python
• Homepage: https://htmlresume-five.vercel.app
• Size: 5.86 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# Resume Generator Project

## Project Overview

This project is a simple Flask web application that allows users to upload a PDF of their LinkedIn profile and convert it into a well-structured HTML resume. The application integrates the OpenAI API to transform the PDF content into HTML using AI-based text generation.

## Technologies Used

- **Flask**: A micro web framework in Python that provides the base for building the web application.

- **OpenAI API**: Used to generate HTML resumes from the extracted text in the LinkedIn PDF.

- **PyPDF2**: A Python library used to extract text from the uploaded PDF files.

- **HTML5/CSS3**: To structure and style the front-end of the resume generator page.

## How the Application Works

1. **Upload LinkedIn PDF**: The user uploads their LinkedIn profile saved as a PDF through the web interface.

2. **API Key Input**: Users are required to provide their OpenAI API key to interact with the GPT-3 model for generating the resume.

3. **PDF Text Extraction**: The PDF file is processed using `PyPDF2`, extracting all the text content from it.

4. **HTML Resume Generation**: The extracted content is passed to the OpenAI API, where a GPT-3 prompt generates a fully structured HTML5 resume based on the PDF content.

5. **Displaying the Resume**: The generated HTML is returned as a web page that the user can view or download.

## Key Components

### `main.py`

- **Upload and Process PDF**: 

  The Flask app accepts PDF uploads, checks file validity, and extracts the PDF's text using the `PdfReader` class from PyPDF2.

  

- **OpenAI API Call**: 

  Using the provided API key, the app interacts with OpenAI's GPT model to convert the extracted text into a structured HTML resume. A well-designed prompt is sent to the API to instruct the model on the expected output.

- **Error Handling**: 

  Error messages are displayed on the frontend in case of an invalid file or an API failure.

### `vercel.json`

- Configures Vercel to deploy the Flask app by specifying the Python handler (`@vercel/python`) and the routing.

### `start.sh`

- A script that starts the Flask application using Gunicorn in a production environment, binding to the specified port.