🚀 TaskFlow Intelligence

TaskFlow Intelligence is a project I created to explore what a modern, scalable API should look like. It's not just another “to-do list,” but a fully containerized ecosystem combining a fast API, background workflows, and a bit of machine learning.

I spent some time making sure the architecture was clean (separation of service and model) and that the asynchronous parts really made sense.

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🛠 Tech Stack & Tools

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🌟 Key Features

• ⚡ Blazing Fast API: Built with FastAPI, handling CRUD for tasks with full Pydantic validation.

• ⚙️ Async Background Workers: Using Celery and Redis to handle heavy stuff (like fetching and parsing external user data) without blocking the main API thread.
• 🧠 Built-in ML Predictor: A scikit-learn model that lives inside the app. It analyzes your task descriptions and automatically guesses if the priority is High or Low.
• 📦 Docker-Ready: One command, and the whole infrastructure (App + Worker + Redis) is alive.
• 🧪 Solid Testing: Covered with unit tests for the core API endpoints.

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🚀 Quick Start (Docker Way)

The easiest way to see this in action is using Docker Compose. Make sure you have Docker running!

1. Spin up the containers:

   docker compose up --build

2. Access the docs: Go to http://localhost:8080/docs. This is where you can play with the API.

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📖 API Overview

Endpoint	Method	What it does?
/tasks	GET	Get all your tasks
/tasks	POST	Create a new one
/tasks/{id}	PUT/DELETE	Update or remove a task
/tasks/parse-users-list	POST	Trigger background parsing (Celery magic)
/predict	POST	Let the AI guess your task priority

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🧠 Machine Learning Engine

If you want to re-train the model or play with the data:

1. Data lives in data/train_data.csv.
2. Run the training script:

   python -m src.ml.train

3. The new model.joblib will be picked up by the API automatically.

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📂 Project Structure

├── src/
│   ├── api/        # Dependencies and API logic
│   ├── models/     # Pydantic schemas (the blueprints)
│   ├── services/   # Business logic (the brain)
│   ├── ml/         # Training scripts and Predictor
│   └── worker/     # Celery configuration and tasks
├── tests/          # Pytest suite
├── data/           # Storage for CSVs and ML models
└── docker-compose.yml

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Made with love for clean code ☕️ (Only ReadMe in project was generated by AI)