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| title: Resume Tailor | |
| emoji: 👁 | |
| colorFrom: yellow | |
| colorTo: purple | |
| sdk: streamlit | |
| sdk_version: 1.38.0 | |
| app_file: app/streamlit_app.py | |
| pinned: false | |
| license: mit | |
| <a id="readme-top"></a> | |
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| <!-- PROJECT LOGO --> | |
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| <div align="center"> | |
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| <h3 align="center">Resume Optimizer</h3> | |
| <p align="center"> | |
| Automation of Resume Tailoring | |
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| <a href="https://huggingface.co/spaces/NvkAnirudh/resume-tailor">Resume Optimizer</a> | |
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| If you find this project helpful, I'd appreciate it if you could give this repo a star ⭐ Cheers! | |
| <!-- TABLE OF CONTENTS --> | |
| <details> | |
| <summary>Table of Contents</summary> | |
| <ol> | |
| <li> | |
| <a href="#introduction">Introduction</a> | |
| <ul> | |
| <li><a href="#built-with">Built With</a></li> | |
| </ul> | |
| </li> | |
| <li><a href="#problem-statement">Problem Statement</a></li> | |
| <li><a href="#project-overview">Project Overview</a></li> | |
| <li><a href="#source-code">Source Code</a></li> | |
| <li><a href="#getting-started">Getting Started</a></li> | |
| <li><a href="#deployment">Deployment</a></li> | |
| <li><a href="#usage">Usage</a></li> | |
| <li><a href="#pricing">Pricing</a></li> | |
| <li><a href="#license">License</a></li> | |
| <li><a href="#contributions">Contributions</a></li> | |
| <li><a href="#contact">Contact</a></li> | |
| <li><a href="#acknowledgments">Acknowledgments</a></li> | |
| </ol> | |
| </details> | |
| ## Introduction | |
| This project automates the tailoring of resumes to a given job description using the Claude 3.5 sonnet model from Anthropic. The goal is to save time and increase efficiency in the job application process. | |
| ### Built With | |
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| <p align="right">(<a href="#readme-top">top</a>)</p> | |
| ## Problem Statement | |
| Tailoring a resume to a job description can be a tedious and time-consuming process. This project aims to consolidate everything in one place, allowing users to simply copy-paste the job description and update their resume accordingly. | |
| <p align="right">(<a href="#readme-top">top</a>)</p> | |
| ## Project Overview | |
| The project consists of the following components: | |
| - A Streamlit application for user input and output | |
| - A Python script for Google Docs API authentication and text styling | |
| - A source code directory containing the following files: | |
| - api_client.py: initializes the Claude 3.5 sonnet model | |
| - config.py: initializes the required Anthropic API key | |
| - pdf_handler.py: reads and returns the attached PDF content | |
| - resume_analyzer.py: initializes the prompt and passes it to the Claude 3.5 API | |
| - text_processing.py: processes the response from Claude to output a structured analysis and updated resume | |
| <p align="right">(<a href="#readme-top">top</a>)</p> | |
| ## Source Code | |
| The source code is organized into the following directories and files: | |
| - ```app```: contains the Streamlit application code | |
| - ```src```: contains the source code files listed above | |
| - ```credentials_creation.py```: generates the credentials.py file for Google Docs API authentication | |
| <p align="right">(<a href="#readme-top">top</a>)</p> | |
| ## Getting Started | |
| To get started in the **local environment**, follow these steps: | |
| 1. Clone the repository using git clone https://github.com/your-username/resume-tailor-automation.git | |
| 2. Install the required packages using pip install -r requirements.txt | |
| 3. Set up your Anthropic API key as an environment variable | |
| 4. Enable the ```Google Docs API``` on GCP console by going to APIs & Services -> Enabled APIs & Services -> 'Search for Google Docs API' | |
| 5. Download the ```credentials.json``` file from GCP console after creating a OAuth2.0 Client ID by going to Credentials -> OAuth 2.0 Client IDs (you may want to fill out the OAuth Consent Screen first) | |
| 6. Once you have all the required keys, you can run ```app/streamlit_app.py``` | |
| <p align="right">(<a href="#readme-top">top</a>)</p> | |
| ## Deployment | |
| The application was deployed on Hugging Face Spaces using GitHub Actions. | |
| To get started in **Hugging Face Spaces**, follow these steps: | |
| 1. Create your own GitHub repo | |
| 2. Create a **GitHub Secret** with your ```HF_Token``` (access token from Hugging Face) | |
| 3. Go to ```Actions``` in your GitHub repo, click **Set up a workflow yourself** and add the yml file from [Hugging Face Spaces documentation](https://huggingface.co/docs/hub/spaces-github-actions) | |
| 4. Since you won't have your ```credentials.json``` (because you put this file in .gitignore so that it doesn't get committed), add ```Google Client ID``` and ```Google Client Secret``` to Spaces environment variables. This is when you need ```credentials_creation.py``` to create a credentials.json file. | |
| 5. Once the above steps are executed properly, your app should be up and running. | |
| <p align="right">(<a href="#readme-top">top</a>)</p> | |
| ## Usage | |
| To use the application, follow these steps: | |
| 1. Copy-paste the job description into the input field | |
| 2. Upload your resume as a PDF file | |
| 3. Click the "Optimize Resume" button to generate an updated resume | |
| <p align="right">(<a href="#readme-top">top</a>)</p> | |
| ## Pricing | |
| The pricing for using the Claude 3.5 sonnet model can be found [here](https://docs.anthropic.com/en/docs/about-claude/models#model-comparison-table) | |
| <p align="right">(<a href="#readme-top">top</a>)</p> | |
| ## License | |
| This project is licensed under [MIT](https://github.com/NvkAnirudh/resume-tailor-automation/blob/main/LICENSE). | |
| <p align="right">(<a href="#readme-top">top</a>)</p> | |
| ## Contributions | |
| Contributions are welcome! If you'd like to contribute to this project, please fork the repository and submit a pull request. | |
| <p align="right">(<a href="#readme-top">top</a>)</p> | |
| ## Contact | |
| [](https://www.linkedin.com/in/nvkanirudh/) | |
| [](mailto:[email protected]) | |
| <p align="right">(<a href="#readme-top">top</a>)</p> | |
| ## Acknowledgements | |
| - Anthropic | |