├── .dockerignore
├── .gitignore
├── Dockerfile
├── README.md
├── app
    ├── main.py
    └── model
    │   ├── model.py
    │   └── trained_pipeline-0.1.0.pkl
├── heroku.yml
└── requirements.txt


/.dockerignore:
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 1 | __pycache__
 2 | *.pyc
 3 | *.pyo
 4 | *.pyd
 5 | .Python
 6 | env/
 7 | venv/
 8 | pip-log.txt
 9 | pip-delete-this-directory.txt
10 | .tox/
11 | .coverage
12 | .coverage.*
13 | .cache
14 | nosetests.xml
15 | coverage.xml
16 | *.cover
17 | *.log
18 | .git
19 | .gitignore
20 | .mypy_cache
21 | .pytest_cache
22 | .hypothesis
23 | .idea


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/.gitignore:
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1 | venv
2 | .DS_Store
3 | 


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/Dockerfile:
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1 | FROM tiangolo/uvicorn-gunicorn-fastapi:python3.9
2 | 
3 | COPY ./requirements.txt /app/requirements.txt
4 | 
5 | RUN pip install --no-cache-dir --upgrade -r /app/requirements.txt
6 | 
7 | COPY ./app /app/app
8 | 


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/README.md:
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 1 | # Deploy ML models with FastAPI, Docker, and Heroku
 2 | 
 3 | ### 1. Develop and save the model with this Colab
 4 | 
 5 | [Open Colab](https://colab.research.google.com/drive/1uaALcaatvxOu42IhQA4r0bahfdpw-Z7v?usp=sharing)
 6 | 
 7 | ### 2. Create Docker container
 8 | 
 9 | ```bash
10 | docker build -t app-name .
11 | 
12 | docker run -p 80:80 app-name
13 | ```
14 | 
15 | ### 3. Create Git repo
16 | 
17 | If you clone this repo this step is not needed. Or you can delete this git repo with `rm -rf .git` and start with a new one:
18 | 
19 | ```bash
20 | git init
21 | git add .
22 | git commit -m "initial commit"
23 | git branch -M main
24 | ```
25 | 
26 | ### 4. Create Heroku project
27 | 
28 | ```bash
29 | heroku login
30 | heroku create your-app-name
31 | heroku git:remote your-app-name
32 | heroku stack:set container
33 | git push heroku main
34 | ```
35 | 


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/app/main.py:
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 1 | from fastapi import FastAPI
 2 | from pydantic import BaseModel
 3 | from app.model.model import predict_pipeline
 4 | from app.model.model import __version__ as model_version
 5 | 
 6 | 
 7 | app = FastAPI()
 8 | 
 9 | 
10 | class TextIn(BaseModel):
11 |     text: str
12 | 
13 | 
14 | class PredictionOut(BaseModel):
15 |     language: str
16 | 
17 | 
18 | @app.get("/")
19 | def home():
20 |     return {"health_check": "OK", "model_version": model_version}
21 | 
22 | 
23 | @app.post("/predict", response_model=PredictionOut)
24 | def predict(payload: TextIn):
25 |     language = predict_pipeline(payload.text)
26 |     return {"language": language}
27 | 


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/app/model/model.py:
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 1 | import pickle
 2 | import re
 3 | from pathlib import Path
 4 | 
 5 | __version__ = "0.1.0"
 6 | 
 7 | BASE_DIR = Path(__file__).resolve(strict=True).parent
 8 | 
 9 | 
10 | with open(f"{BASE_DIR}/trained_pipeline-{__version__}.pkl", "rb") as f:
11 |     model = pickle.load(f)
12 | 
13 | 
14 | classes = [
15 |     "Arabic",
16 |     "Danish",
17 |     "Dutch",
18 |     "English",
19 |     "French",
20 |     "German",
21 |     "Greek",
22 |     "Hindi",
23 |     "Italian",
24 |     "Kannada",
25 |     "Malayalam",
26 |     "Portugeese",
27 |     "Russian",
28 |     "Spanish",
29 |     "Sweedish",
30 |     "Tamil",
31 |     "Turkish",
32 | ]
33 | 
34 | 
35 | def predict_pipeline(text):
36 |     text = re.sub(r'[!@#$(),\n"%^*?\:;~`0-9]', " ", text)
37 |     text = re.sub(r"[[]]", " ", text)
38 |     text = text.lower()
39 |     pred = model.predict([text])
40 |     return classes[pred[0]]
41 | 


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/app/model/trained_pipeline-0.1.0.pkl:
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https://raw.githubusercontent.com/AssemblyAI-Community/ml-fastapi-docker-heroku/f36f43fe739fe8384e6fb41b754e3cc496e631ff/app/model/trained_pipeline-0.1.0.pkl


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/heroku.yml:
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1 | build:
2 |   docker:
3 |     web: Dockerfile


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/requirements.txt:
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1 | scikit-learn==1.0.2


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