├── .gitignore
├── requirements.txt
├── model_tree.pkl
├── tutorial.py
├── README.md
├── main.py
└── music.py


/.gitignore:
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1 | .vscode
2 | __pycache__/


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/requirements.txt:
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1 | fastapi
2 | uvicorn
3 | sklearn


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/model_tree.pkl:
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https://raw.githubusercontent.com/kaustubhgupta/FastAPI-Demo/HEAD/model_tree.pkl


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/tutorial.py:
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 1 | import uvicorn
 2 | from fastapi import FastAPI
 3 | 
 4 | app = FastAPI()
 5 | 
 6 | @app.get("/")
 7 | def home_end_point(name: str):
 8 |     return {"message": f"Hello! {name}"}
 9 | 
10 | if __name__ == "__main__":
11 |     uvicorn.run(app, host='127.0.0.1', port=8000, debug=True)


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/README.md:
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 1 | # FastAPI-Demo
 2 | 
 3 | This repository holds the code used for article in Analytics Vidhya Blogathon. To read the corresponding article then head over to this link:
 4 | https://www.analyticsvidhya.com/blog/2020/11/fastapi-the-right-replacement-for-flask/
 5 | 
 6 | ## Directory Explaination
 7 | 
 8 | - [tutorial.py](./tutorial.py) file is the one used for FastAPI demo code in article
 9 | - [music.py](./music.py) file is the class layout (Pydantic model) for data validation
10 | - [main.py](./main.py) file is the music genre classifier FastAPI code
11 | - [model_tree.pkl](./model_tree.pkl) is the decision tree classifier file
12 | 


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/main.py:
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 1 | import uvicorn
 2 | import pickle
 3 | from fastapi import FastAPI
 4 | from music import Music
 5 | 
 6 | 
 7 | app = FastAPI()
 8 | pickle_in = open("model_tree.pkl", "rb")
 9 | model = pickle.load(pickle_in)
10 | 
11 | 
12 | @app.get('/')
13 | def index():
14 |     return {'message': 'This is the homepage of the API '}
15 | 
16 | 
17 | @app.post('/predict')
18 | def get_music_category(data: Music):
19 |     received = data.dict()
20 |     acousticness = received['acousticness']
21 |     danceability = received['danceability']
22 |     energy = received['energy']
23 |     instrumentalness = received['instrumentalness']
24 |     liveness = received['liveness']
25 |     speechiness = received['speechiness']
26 |     tempo = received['tempo']
27 |     valence = received['valence']
28 |     pred_name = model.predict([[acousticness, danceability, energy,
29 |                                 instrumentalness, liveness, speechiness, tempo, valence]]).tolist()[0]
30 |     return {'prediction': pred_name}
31 | 
32 | 
33 | if __name__ == '__main__':
34 |     uvicorn.run(app, host='127.0.0.1', port=8000, debug=True)
35 | 


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/music.py:
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 1 | from pydantic import BaseModel, Field
 2 | 
 3 | 
 4 | class Music(BaseModel):
 5 |     acousticness: float = Field(..., description="Here is the description of the input to be sent")
 6 |     danceability: float = Field(..., description="Here is the description of the input to be sent")
 7 |     energy: float = Field(..., description="Here is the description of the input to be sent")
 8 |     instrumentalness: float = Field(..., description="Here is the description of the input to be sent")
 9 |     liveness: float = Field(..., description="Here is the description of the input to be sent")
10 |     speechiness: float = Field(..., description="Here is the description of the input to be sent")
11 |     tempo: float = Field(..., description="Here is the description of the input to be sent")
12 |     valence: float = Field(..., description="Here is the description of the input to be sent")
13 |     class Config:
14 |         schema_extra = {
15 |             "example": {
16 |                 "acousticness": 0.344719513,
17 |                 "danceability": 0.758067547,
18 |                 "energy": 0.323318405,
19 |                 "instrumentalness": 0.0166768347,
20 |                 "liveness": 0.0856723112,
21 |                 "speechiness": 0.0306624283,
22 |                 "tempo": 101.993,
23 |                 "valence": 0.443876228 ,
24 |             }
25 |         }
26 | 


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