https://github.com/ivangolt/text_tone_service

https://github.com/ivangolt/text_tone_service

Last synced: 6 days ago
JSON representation

• Host: GitHub
• URL: https://github.com/ivangolt/text_tone_service
• Owner: ivangolt
• License: mit
• Created: 2024-05-31T16:28:11.000Z (6 months ago)
• Default Branch: main
• Last Pushed: 2024-07-08T13:05:45.000Z (4 months ago)
• Last Synced: 2024-07-08T16:18:04.221Z (4 months ago)
• Language: Python
• Size: 207 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# Model Service Api with Streamlit frontend

Example The ML service is a web application that provides an API for interacting with a machine learning model. It allows users to send queries with prediction data and get results back.

**Startup logic:**

When launched, the application initializes FastAPI, which handles HTTP requests. The app also connects to the machine learning model and loads it into memory for use in making predictions.

```

.

├── Docker

│   └── frontend

|        └── Dockerfile         # Docker image for frontend app

|   └── backend

|         └── Dockerfile        # Docker image for backend app

├── frontend

|      └── tone_app.py          # Streamlit app file for frontend part of app   

├── docker-compose.yml          # Docker container managing

├── pyproject.toml              # Dependencies

└── src

    ├── app.py                  # Main app, FastAPI initializing

    ├── api                     # Package with API routes

    │   ├── __init__.py

    │   └── routes              # Package with API routes

    │       ├── __init__.py

    │       ├── healthcheck.py  # Route to check the srvice status

    │       ├── predict.py      # Route for model predictions

    │       └── router.py       # Main router

    ├── schemas                 # Package with data models

    │   ├── __init__.py

    │   ├── healthcheck.py      # Model for service state responses

    │   └── requests.py         # Model for input requests to the API

    └── services                # Package with ML model

        ├── __init__.py

        ├── model.py            # ML model with prediction

        └── utils.py            # Supporting utilities

```

## Getting started with Docker Compose

`docker-compose up --build`

Web-server on

`http://localhost:8000`

UI on

`http://localhost:8000/docs`

App on 

`http://localhost:8501`

## Running local

`pip install --no-cache-dir poetry`

`poetry install --no-dev`

`poetry run uvicorn src.app:app --host localhost --port 8000`

and

`poetry run streamlit run frontend/tone_app.py`

# Example of work

![](docs/streamlit_service.png)