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PyAdvisor - Your career advisor based on your github data & given info. Made using Hugging Face Model and Python

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PyAdvisor - [Star 🌟 & Follow]

  • Welcome to PyAdvisor, your personal career advisor tailored to your Github profile and provided information. Follow the steps below to get started:

🚀 Step 1: Setup

1.1. Clone PyAdvisor Repository

First, clone the PyAdvisor repository to your local machine:

git clone https://github.com/drix10/PyAdvisor.git

1.2. Install Requirements

Navigate to the cloned directory and install the required dependencies using pip:

cd PyAdvisor
pip install -r requirements.txt

🚀 Step 2: Obtaining Your Github Access Token

To fetch data from your Github profile, you'll need an access token with read access. Here's how you can obtain it:

2.1. Generate a Personal Access Token

  1. Go to your Github settings: https://github.com/settings
  2. Navigate to "Developer settings" > "Personal access tokens".
  3. Click on "Generate new token".
  4. Give your token a descriptive name and select the scopes: repo (for private repositories) and read:user (for accessing user profile data).
  5. Click on "Generate token" and copy the generated token.

2.2. Input Your Github Credentials

Run the PyAdvisor script and input your Github username and access token when prompted:

python PyAdvisor.py

🚀 Step 3: Provide Your Information

After providing your Github credentials, you'll be asked to input your skills, interests, and goals. Follow the prompts and enter the requested information.

🚀 Step 4: Generating Results

Once all information is provided, PyAdvisor will process your data and generate personalized career advice based on your Github profile and input.

Example

🚀 Additional Notes

  • Privacy: Your Github access token is only used to fetch data from your profile and is not stored or shared anywhere.
  • Feedback: I welcome any feedback or suggestions for improving PyAdvisor. For any issues contact me on discord: @drix10.

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PyAdvisor - Your career advisor based on your github data & given info. Made using Hugging Face Model and Python

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