Task Manager - AI-Powered Role-Task Matching System

An task management system that leverages AI to analyze project requirements, generate interview questions, and match suitable tasks to specific roles using semantic similarity.

🌟 Features

• AI-Powered Project Analysis: Uses Gemini to analyze project details and extract relevant information
• Dynamic Interview Generation: Generates 5 simple, foundational interview questions to assess their basic understanding, learning enthusiasm, and natural inclinations toward different technical roles
• Role-Task Matching: Employs a fine-tuned Siamese neural network to match tasks with appropriate roles (Backend Developer, Frontend Developer, Project Manager, UX/UI Designer) based on semantic similarity
• Comprehensive Task Database: Includes a dataset of over 20,000 categorized tasks with associated skills

🏗️ System Architecture

The system consists of four main components:

1. API Chatbot (apichatbot.py): Handles AI communication and project analysis
2. Role-Task Matcher (botcompae.py): Performs semantic matching using pre-trained models
3. Data Processing (data.py): Manages dataset operations and preprocessing
4. Feature Engineering (feature_engineering.py): Handles data transformation and feature extraction

📁 Project Structure

task-manager/
├── main.py                     # Main application entry point
├── dataset/                    # Data files
│   ├── hr_dashboard_data.csv   # HR dashboard data
│   ├── Task Catagories.csv     # Task categories and skills mapping
│   └── updated_hr_data.csv     # Updated HR dataset
├── models/                     # Pre-trained models
│   └── role_task_siamese_v1/   # Siamese network for role-task matching
├── src/                        # Source code modules
│   ├── apichatbot.py          # AI chatbot implementation
│   ├── botcompae.py           # Role-task matching logic
│   ├── data.py                # Data handling utilities
│   ├── feature_engineering.py # Feature processing
│   └── suitabilty.py          # Suitability assessment
└── README.md                  # Project documentation

🚀 Getting Started

Prerequisites

• Python 3.8 or higher
• Google Generative AI API key
• Required Python packages (see installation section)

Installation

1. Clone the repository:

   git clone https://github.com/Gun249/task-manager.git
   cd task-manager

2. Install required dependencies:

   pip install -r requirements.txt

3. Download spaCy language model:

   python -m spacy download en_core_web_lg

4. Set up environment variables: Create a .env file in the root directory and add your API key:

   apikey=your_google_generative_ai_api_key_here
   

5. Verify model files: Ensure the pre-trained Siamese model is in the models/role_task_siamese_v1/ directory.

Usage

1. Run the application:

   python main.py

2. Provide project details: When prompted, enter a detailed description of your project requirements.

3. Answer interview questions: The system will generate 5 simple, foundational questions based on your project. These questions help the AI understand your basic technical understanding, interests, and problem-solving approach without requiring deep existing knowledge.

4. Review results: The system will output:

   • Recommended role for your project
   • Matching tasks with similarity scores

🔧 Configuration

Model Configuration

The system uses a pre-trained Siamese neural network located in models/role_task_siamese_v1/. You can customize the matching behavior by adjusting:

• Similarity Threshold: Minimum cosine similarity score for task matching (default: 0.4)
• Model Path: Path to the sentence transformer model

API Configuration

The system integrates with Gemini. Configure your API settings in the .env file:

apikey=your_api_key_here

📊 Dataset Information

The system utilizes a comprehensive dataset containing:

• 20,000+ Task Descriptions: Categorized by role and required skills
• Role Categories: Backend Developer, Frontend Developer, etc.
• Skill Mappings: Technologies and frameworks associated with each task

🤖 AI Components

Chatbot (apichatbot.py)

• Analyzes project requirements using Gemini
• Generates 5 simple, foundational interview questions tailored
• Creates questions designed to assess basic understanding, learning enthusiasm, and problem-solving approach
• Extracts tasks aligned with four major roles: Backend Developer, Frontend Developer, Project Manager (PM), and UX/UI Designer
• Processes user responses to determine the most suitable role

Role-Task Matcher (botcompae.py)

• Uses SentenceTransformer for semantic similarity
• Employs cosine similarity for task-role matching
• Configurable similarity thresholds
• Returns ranked task recommendations

🔍 Example Workflow

1. Input: "I need to build a web application with user authentication and data visualization"

2. AI Analysis: System generates 5 simple, foundational interview questions :

   • Basic understanding and enthusiasm for learning
   • Initial technical inclinations and preferences
   • Foundational problem-solving mindset
   • Natural curiosity across Backend, Frontend, PM, and UX/UI concepts
   • How students articulate their thoughts on basic technical concepts

3. Role Recommendation: "Backend Developer"

4. Task Matching:

   • Implement user authentication (Score: 0.8542)
   • Design database schema (Score: 0.7891)
   • Create API endpoints (Score: 0.7645)

🛠️ Development

Adding New Features

1. Extend the dataset: Add new task categories in dataset/Task Catagories.csv
2. Customize prompts: Modify AI prompts in apichatbot.py
3. Adjust matching logic: Update similarity calculations in botcompae.py

Testing

Run the application with sample data to verify functionality:

python main.py

🙏 Acknowledgments

• Google Generative AI for natural language processing
• Sentence Transformers library for semantic similarity
• The open-source community for various dependencies and tools

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Note: This system is designed for educational and research purposes. Ensure you have proper API keys and follow the terms of service for all integrated services.