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+# Use the official Python 3.9 image
+FROM python:3.8.6
+ 
+# Set the working directory to /code
+WORKDIR /code
+ 
+# Copy the current directory contents into the container at /code
+COPY ./requirements.txt /code/requirements.txt
+ 
+# Install requirements.txt 
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+ 
+# Set up a new user named "user" with user ID 1000
+RUN useradd -m -u 1000 user
+# Switch to the "user" user
+USER user
+# Set home to the user's home directory
+ENV HOME=/home/user \\
+    PATH=/home/user/.local/bin:$PATH
+ 
+# Set the working directory to the user's home directory
+WORKDIR $HOME/app
+ 
+# Copy the current directory contents into the container at $HOME/app setting the owner to the user
+COPY --chown=user . $HOME/app
+ 
+# Start the FastAPI app on port 7860, the default port expected by Spaces
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+# 返回图片测试
+from sklearn.linear_model import LinearRegression
+from sklearn.neural_network import MLPRegressor
+import lightgbm as lgb
+from xgboost import XGBRegressor
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.preprocessing import StandardScaler
+from sklearn.model_selection import train_test_split
+import pandas as pd
+from fastapi.middleware.cors import CORSMiddleware  # 跨域
+from fastapi import FastAPI, Response, BackgroundTasks
+import json
+import matplotlib.pyplot as plt
+import io
+import matplotlib
+matplotlib.use('AGG')
+
+app = FastAPI()
+
+# 配置跨域白名单
+origins = [
+    "http://127.0.0.1:5500"
+]
+
+# 图片测试
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=origins,
+    allow_credentials=True,
+    allow_methods=["POST"],
+    allow_headers=["*"],
+)
+
+def create_img():
+    plt.rcParams['figure.figsize'] = [7.50, 3.50]
+    plt.rcParams['figure.autolayout'] = True
+    plt.plot([1, 2])
+    img_buf = io.BytesIO()
+    plt.savefig(img_buf, format='png')
+    plt.close()
+    return img_buf
+
+@app.get('/png')
+async def get_img(background_tasks: BackgroundTasks):
+    img_buf = create_img()
+    # get the entire buffer content
+    # because of the async, this will await the loading of all content
+    bufContents: bytes = img_buf.getvalue()
+    background_tasks.add_task(img_buf.close)
+    headers = {'Content-Disposition': 'inline; filename="out.png"'}
+    return Response(bufContents, headers=headers, media_type='image/png')
+
+
+# 机器学习测试
+# 多元线性回归
+@app.post("/mlr")
+async def mlr():
+    # 引入excel数据
+    # 可能存在空行问题，dropna(axis=0)删除空行
+    df = pd.read_csv("./1.csv").dropna(axis=0)
+    # 取xy
+    x = df.iloc[:, :12]
+    # y = df.loc[:, "BSR"]
+    y = df.loc[:, "SBR"]
+    # 划分数据集
+    x_train, x_test, y_train, y_test = train_test_split(
+        x, y, test_size=0.3, random_state=0)
+
+    # 标准化
+    standardscaler = StandardScaler()
+    standardscaler.fit(x_train)
+    x_train = standardscaler.transform(x_train)
+    x_test = standardscaler.transform(x_test)
+
+    # 多元线性回归
+    model = LinearRegression()
+    model.fit(x_train, y_train)
+
+    # 测试样本预测
+    y_pred = model.predict(x_test)
+    return json.dumps(y_pred.tolist())
+
+# 随机森林
+@app.post("/rf")
+async def rf():
+    # 引入excel数据
+    # 可能存在空行问题，dropna(axis=0)删除空行
+    df = pd.read_csv("./1.csv").dropna(axis=0)
+    # 取xy
+    x = df.iloc[:, :12]
+    # y = df.loc[:, "BSR"]
+    y = df.loc[:, "SBR"]
+    # 划分数据集
+    x_train, x_test, y_train, y_test = train_test_split(
+        x, y, test_size=0.3, random_state=0)
+
+    # 标准化
+    standardscaler = StandardScaler()
+    standardscaler.fit(x_train)
+    x_train = standardscaler.transform(x_train)
+    x_test = standardscaler.transform(x_test)
+
+    # 随机森林
+    model = RandomForestRegressor()
+    model.fit(x_train, y_train)
+
+    # 测试样本预测
+    y_pred = model.predict(x_test)
+    return json.dumps(y_pred.tolist())
+
+# BP神经网络
+@app.post("/bpn")
+async def rf():
+    # 引入excel数据
+    # 可能存在空行问题，dropna(axis=0)删除空行
+    df = pd.read_csv("./1.csv").dropna(axis=0)
+    # 取xy
+    x = df.iloc[:, :12]
+    # y = df.loc[:, "BSR"]
+    y = df.loc[:, "SBR"]
+    # 划分数据集
+    x_train, x_test, y_train, y_test = train_test_split(
+        x, y, test_size=0.3, random_state=0)
+
+    # 标准化
+    standardscaler = StandardScaler()
+    standardscaler.fit(x_train)
+    x_train = standardscaler.transform(x_train)
+    x_test = standardscaler.transform(x_test)
+
+    # BP神经网络
+    model = MLPRegressor(hidden_layer_sizes=(10,), random_state=10,learning_rate_init=0.1)  # BP神经网络回归模型
+    model.fit(x_train,y_train)  # 训练模型
+
+    # 测试样本预测
+    y_pred = model.predict(x_test)
+    return json.dumps(y_pred.tolist())
+
+# XGBoost
+@app.post("/xgboost")
+async def rf():
+    # 引入excel数据
+    # 可能存在空行问题，dropna(axis=0)删除空行
+    df = pd.read_csv("./1.csv").dropna(axis=0)
+    # 取xy
+    x = df.iloc[:, :12]
+    # y = df.loc[:, "BSR"]
+    y = df.loc[:, "SBR"]
+    # 划分数据集
+    x_train, x_test, y_train, y_test = train_test_split(
+        x, y, test_size=0.3, random_state=0)
+
+    # 标准化
+    standardscaler = StandardScaler()
+    standardscaler.fit(x_train)
+    x_train = standardscaler.transform(x_train)
+    x_test = standardscaler.transform(x_test)
+
+    # XGBoost
+    model = XGBRegressor(max_depth=5, learning_rate=0.1, n_estimators=160, objective='reg:gamma')
+    model.fit(x_train, y_train)
+
+    # 测试样本预测
+    y_pred = model.predict(x_test)
+    return json.dumps(y_pred.tolist())
+
+# LightGBM
+@app.post("/lightgbm")
+async def rf():
+    # 引入excel数据
+    # 可能存在空行问题，dropna(axis=0)删除空行
+    df = pd.read_csv("./1.csv").dropna(axis=0)
+    # 取xy
+    x = df.iloc[:, :12]
+    # y = df.loc[:, "BSR"]
+    y = df.loc[:, "SBR"]
+    # 划分数据集
+    x_train, x_test, y_train, y_test = train_test_split(
+        x, y, test_size=0.3, random_state=0)
+
+    # 标准化
+    standardscaler = StandardScaler()
+    standardscaler.fit(x_train)
+    x_train = standardscaler.transform(x_train)
+    x_test = standardscaler.transform(x_test)
+
+    # LightGBM
+    model = lgb.LGBMRegressor(objective='regression',num_leaves=31,learning_rate=0.05,n_estimators=20)
+    model.fit(x_train, y_train)
+
+    # 测试样本预测
+    y_pred = model.predict(x_test)
+    return json.dumps(y_pred.tolist())
+

requirements.txt

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+fastapi==0.95.1
+lightgbm==3.3.5
+matplotlib==3.5.1
+pandas==1.5.3
+scikit_learn==1.1.1
+xgboost==1.7.5