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import imghdr |
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from os import name |
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from fastapi import FastAPI, Response, UploadFile |
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from pydantic import BaseModel |
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import time |
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import pickle |
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import numpy as np |
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import uvicorn |
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from models import classe |
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app = FastAPI() |
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pickle_in = open("classifier.pkl","rb") |
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classifier = pickle.load(pickle_in) |
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@app.get("/") |
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def index(): |
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return {"hello": "FastAPI"} |
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@app.get('/{name}') |
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def get_name(name: str): |
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return {'message': f'hello, {name}'} |
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@app.post('/predict') |
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def predict_species(data: classe): |
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Sepal_Length = data.Sepal_Length |
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Sepal_Width = data.Sepal_Width |
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Petal_Length = data.Petal_Length |
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Petal_Width = data.Petal_Width |
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prediction = classifier.predict([[Sepal_Length, Sepal_Width, Petal_Length, Petal_Width]]) |
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if prediction[0] == 0: |
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species = "setosa" |
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elif prediction[0] == 1: |
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species = "virginica" |
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elif prediction[0] == 2: |
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species = "versicolor" |
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else: |
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species = "unknown" |
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return {'prediction': species} |
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if __name__ == "__main__": |
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uvicorn.run(app, host="127.0.0.1", port=8000) |
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