如何在FastAPI中使用机器学习模型进行数据预测

pip install fastapipip install uvicornpip install scikit-learn

from sklearn.linear_model import LinearRegressionimport numpy as np# 构建模型model = LinearRegression()# 准备训练数据X_train = np.array(...).reshape(-1, 1)  # 输入特征y_train = np.array(...)  # 目标变量# 训练模型model.fit(X_train, y_train)

from fastapi import FastAPIfrom pydantic import BaseModel# 导入模型from sklearn.linear_model import LinearRegression

class InputData(BaseModel):    input_value: floatclass OutputData(BaseModel):    output_value: float

app = FastAPI()

@app.post('/predict')async def predict(input_data: InputData):    # 调用模型进行预测    input_value = input_data.input_value    output_value = model.predict([[input_value]])    # 构造输出数据    output_data = OutputData(output_value=output_value[0])    return output_data

uvicorn main:app --reload

{    "input_value": 5.0}

{    "output_value": 10.0}

from fastapi import FastAPIfrom pydantic import BaseModelfrom sklearn.linear_model import LinearRegressionimport numpy as np# 创建模型和训练数据model = LinearRegression()X_train = np.array([1, 2, 3, 4, 5]).reshape(-1, 1)y_train = np.array([2, 4, 6, 8, 10])model.fit(X_train, y_train)# 定义输入输出数据模型class InputData(BaseModel):    input_value: floatclass OutputData(BaseModel):    output_value: float# 创建FastAPI应用实例app = FastAPI()# 定义数据预测的路由@app.post('/predict')async def predict(input_data: InputData):    input_value = input_data.input_value    output_value = model.predict([[input_value]])    output_data = OutputData(output_value=output_value[0])    return output_data