CareerCraft AI

CareerCraft AI is an intelligent application designed to provide personalized career recommendations using advanced AI technologies. By leveraging a language model (OpenAI API) and local data persistence, the application offers features such as focus area suggestions and career path recommendations tailored to user profiles.

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Key Features and Highlights

• Chatbot-Driven Profile Building:
  Engage with an interactive chatbot to build your profile. The chatbot gathers essential information like age, passions, and interests through targeted questions, inferring personality traits and career preferences.

• Personality and Interest Analysis:
  The backend analyzes user responses to infer key personality traits and interests. This data forms the foundation of personalized career recommendations.

• AI-Powered Focus Area Recommendations:
  Based on your profile, the application provides tailored focus area suggestions with explanations on why each area aligns with your unique traits.

• Career Recommendations Based on Focus:
  After selecting a focus area, receive curated career paths linked to that area. These recommendations are drawn from a predefined knowledge base.

• Essential Soft Skills:
  Alongside career suggestions, the app highlights evergreen soft skills valuable across professions to encourage personal development.

• Persistent User Profiles:
  User data is saved in a user_profiles.json file, ensuring seamless continuity across sessions.

• Modular Architecture:
  Built with a clear separation between frontend (Streamlit) and backend (FastAPI), promoting maintainability and scalability.

• Configurable Language Model:
  The backend integrates with the OpenAI API, allowing for future upgrades or customization of the language model.

• Extensive Knowledge Base:
  Incorporates predefined lists of focus areas, career paths, and soft skills to enhance recommendation accuracy.

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Code Repository Structure

CareerCraft-AI/
├── backend/
│ ├── app.py # FastAPI application entry point
│ ├── prompts/
│ │ └── prompts_templates.py # Prompt templates for the language model
│ ├── utils/
│ │ ├── constants.py # Application-wide constants (focus areas, careers, etc.)
│ │ ├── helper_functions.py # Utility functions (parsing LLM responses)
│ │ └── model_generations.py # Functions for interacting with the language model
│ ├── user_profiles.json # File to persist user profile data
│ └── requirements.txt # Backend dependencies
├── frontend/
│ ├── app.py # Streamlit application entry point
│ └── requirements.txt # Frontend dependencies
├── .env # Environment variables (e.g., OpenAI API key)
└── README.md # This file

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Setup

Prerequisites

• Python 3.7 or higher
• pip (Python package installer)
• OpenAI API key

Installation

1. Clone the repository: git clone <repository_url> cd CareerCraft-AI

2. Create a virtual environment and activate it: python -m venv venv source venv/bin/activate # On macOS/Linux

venv\Scripts\activate # On Windows

3. Install the required dependencies: pip install -r requirements.txt

4. Set up environment variables:

• Create a .env file in the root directory.
• Add your OpenAI API key:

  OPENAI_API_KEY=your-openai-api-key
  

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Usage

Running the Application Locally

1. Start the FastAPI backend: cd backend uvicorn app:app --reload

2. Start the Streamlit frontend (in a separate terminal): cd frontend streamlit run app.py

3. Access the application through the Streamlit web interface at http://localhost:8501.

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API Endpoints

Available Endpoints:

1. GET /users/{user_id}
   Retrieves the complete learner profile for a given user_id.

2. POST /chatbot/interact
   Sends a message to the chatbot for a given user_id and receives a response, updating the user's profile and assessment status.

3. POST /recommend/focus
   Generates personalized focus area recommendations for a given user_id.

4. POST /recommend/careers
   Provides career recommendations and soft skills for a given user_id and chosen_focus_area.

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Example Requests:

Get User Profile:

curl -X 'GET' 'http://127.0.0.1:8000/users/Rakesh_11' -H 'accept: application/json'

Chatbot Interaction:

curl -X 'POST' 'http://127.0.0.1:8000/chatbot/interact' -H 'accept: application/json' -H 'Content-Type: application/json' -d '{ "user_id": "Rakesh_11", "message": "Tell me about your energy levels in social situations." }'

Get Focus Recommendation:

curl -X 'POST' 'http://127.0.0.1:8000/recommend/focus' -H 'accept: application/json' -H 'Content-Type: application/json' -d '{ "user_id": "Rakesh_11" }'

Get Career Recommendation:

curl -X 'POST' 'http://127.0.0.1:8000/recommend/careers' -H 'accept: application/json' -H 'Content-Type: application/json' -d '{ "user_id": "Rakesh_11", "chosen_focus_area": "Technology" }'

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Contributing

Contributions are welcome! Please open an issue or submit a pull request for any improvements or bug fixes.

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License

This project is licensed under the MIT License. See the LICENSE file for details.