AnomaliFlow

This application provides a robust framework for managing machine learning tasks using a combination of FastAPI, Celery, Streamlit, MongoDB, Prefect and Redis. The system allows users to upload datasets, select features, choose machine learning algorithms, and execute tasks asynchronously. Results and task statuses are displayed in real-time, leveraging the power of modern web technologies and scalable background task processing.

Features

File Upload, Cloud Storage Mount, and Data Preview
Feature Engineering
ML/DL Algorithms Selection
Asynchronous Task & Workflow Execution
Real-time Status Updates
Results Display
API Documentation

components

Swagger UI: API Documentation
Celery: asynchronous task queue/job queue based on distributed message passing
FastAPI: A modern, fast, web framework for building APIs
MongoDB: NoSQL Database
Redis: in-memory data structure store
Streamlit: open-source app framework for Machine Learning and Data Science
Prefect: Data pipeline workflow ochestration

run

docker-compose up –build -d

docker-compose –profile prefect_server up

access the application

Streamlit Interface: Open your browser and navigate to http://localhost:8501 to access the Streamlit interface.
Dash Interface: Open your browser and navigate to http://localhost:8050 to access the Dash interface.
API Documentation: Visit http://localhost:8000/docs to view the interactive API documentation provided by FastAPI.
Prefect Interface: Open your browser and navigate to http://localhost:4200 to access the Prefect interface.

ports

MongoDB: 27017 (use Compass)
Redis: 6379
FastAPI: 8000
Streamlit: 8501
Prefect: 4200