| import numpy as np |
| np.random.seed(0) |
| import pickle |
| from sklearn.compose import ColumnTransformer |
| from sklearn.datasets import fetch_openml |
| from sklearn.pipeline import Pipeline |
| from sklearn.impute import SimpleImputer |
| from sklearn.preprocessing import StandardScaler, OneHotEncoder |
| from sklearn.linear_model import LogisticRegression |
| from sklearn.model_selection import train_test_split |
|
|
| from sklearn import tree |
|
|
| from fastapi import FastAPI, HTTPException |
| from fastapi.responses import HTMLResponse |
| from pydantic import BaseModel |
| from typing import List |
|
|
| class InputData(BaseModel): |
| data: List[float] |
| |
| |
| app = FastAPI() |
|
|
| def build_model(): |
| with open('miarbol.pkl', 'rb') as fid: |
| miarbol = pickle.load(fid) |
| return miarbol |
| |
| miarbol = build_model() |
|
|
| |
| @app.post("/predict/") |
| async def predict(data: InputData): |
| print(f"Data: {data}") |
| global miarbol |
| try: |
| |
| input_data = np.array(data.data).reshape( |
| 1, -1 |
| ) |
| prediction = miarbol.predict(input_data).round() |
| return {"prediction": prediction.tolist()} |
| except Exception as e: |
| raise HTTPException(status_code=500, detail=str(e)) |
