搭建python flask服務的步驟
1、安裝相關的包
具體參考https://blog.csdn.net/weixin_42126327/article/details/127642279
1、安裝conda環境和相關包
# 一、安裝conda
# 1、首先，前往Anaconda官網（https://www.anaconda.com/products/individual）下載適合您的Linux版本的Anaconda安裝文件。# 2、下載Anaconda安裝文件
wget https://repo.anaconda.com/archive/Anaconda3-2023.07-2-Linux-x86_64.sh
# 3、安裝conda
bash Anaconda3-2023.07-2-Linux-x86_64.sh# 4、conda --version
如果輸出了Anaconda的版本號，則安裝成功。## 二、創建canda的虛擬開發環境 py36# 1、創建canda的虛擬開發環境 py36
conda create --name py36 python=3.6# 2、進入虛擬開發環境
conda activate py36# 3、退出虛擬環境
conda deactivate## 三、安裝flask相關的包
conda install flask
conda install scikit-learn2、搭建flask服務代碼
1、訓練模型并保存模型
model.py
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_iris
import pickle# 加載iris數據集，并進行最簡單的訓練過程，可以自行擴展
iris = load_iris()
X = iris.data
y = iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
classifier = RandomForestClassifier()
classifier.fit(X_train, y_train)
print(classifier)將模型保存為pkl格式，準備應用層調用
pickle.dump(classifier, open("./model.pkl", "wb"))2、啟動flask服務
app_demo.py
import numpy as np
from flask import Flask, request, jsonify, render_template
from flask_cors import CORS
import pickleapp = Flask(__name__,template_folder='../templates')model = pickle.load(open('model.pkl', 'rb'))
@app.route('/')
def home():return render_template('index.html')@app.route('/results', methods=['POST'])
def results():data = request.get_json(force=True)print(data)prediction = model.predict([np.array(list(data.values()))])# 將預測結果放在字典中output = {'prediction': int(prediction[0])}# 將字典轉換為 JSON 格式并返回return jsonify(output)
if __name__ == "__main__":app.run(debug=True) 3、調用flask服務的客戶端代碼
request_demo.py python客服端代碼
import requests
url = 'http://localhost:5000/results'
r = requests.post(url,json={'Sepal_Length':5,'Sepal_Width':2, 'Petal_Length':4, 'Petal_Width':2})
print(r.json())FlaskClient.scala scala客戶端代碼
package com.demoimport java.net.URLimport org.apache.http.client.methods.HttpPost
import org.apache.http.entity.StringEntity
import org.apache.http.impl.client.HttpClientBuilder
import org.json4s._
import org.json.JSONObject
import org.apache.http.util.EntityUtils
import org.json4s.jackson.JsonMethods.parseobject FlaskClient {implicit val formats = DefaultFormatsdef main(args: Array[String]): Unit = {val url = new URL("http://localhost:5000/results")// 構造JSON對象val data = new JSONObject()data.put("feature1", 1)data.put("feature2", 2)data.put("feature3", 3)data.put("feature4", 4)// 將JSON對象轉換為字符串val json = data.toString()val post = new HttpPost(url.toURI)post.setEntity(new StringEntity(json))post.setHeader("Content-type", "application/json")val client = HttpClientBuilder.create.buildval response = client.execute(post)val entity = response.getEntityval result = EntityUtils.toString(entity)val prediction = (parse(result) \ "prediction").extract[Int]println(prediction)}
}http客戶端代碼 templates/index.html
<!-- index.html -->
<!DOCTYPE html>
<html >
<head><meta charset="UTF-8"><title>ML API</title>
</head>
<body>
<div class="login"><h1>Flower Class Prediction</h1><form action="{{ url_for('predict')}}"method="post"> <!-- 這一塊是用戶輸入自定義數據 --><input type="text" name="Sepal_Length" placeholder="Sepal_Length" required="required"/><input type="text" name="Sepal_Width" placeholder="Sepal_Width" required="required" /><input type="text" name="Petal_Length" placeholder="Petal_Length" required="required"/><input type="text" name="Petal_Width" placeholder="Petal_Width" required="required" /><button type="submit" class="btn btn-primary btn-block btn-large">Predict</button></form><br>{{ prediction_text }} <!-- 這一塊是模型輸出結果 -->
</div>
</body>
</html>3、flask服務的運行
1、調試環境
# 1、啟動flask服務
python app_demo.py
# 2、客戶端請求flask服務
# 2.1 python客戶端
python request_demo.py # 2.2 scala 客戶端java -cp target/xx-2.0.0-SNAPSHOT-jar-with-dependencies.jar com.xx.FlaskClient# 2.3 http客戶端
http://127.0.0.1:5000/predict 2、生產環境部署
uWSGI：uWSGI是一個高性能的WSGI服務器，支持多種編程語言和應用程序框架，包括Flask。可以使用uWSGI來部署Flask服務
uwsgi安裝
conda install uwsgi --no-deps # 使用--no-deps選項：這將跳過依賴項部署 參考：https://juejin.cn/post/7014036187937505317
配置文件uwsgi.ini 文件
`[uwsgi]http = 127.0.0.1:5000wsgi-file = app_demo.pycallable = app`
準備好配置文件后
# 啟動uwsgi服務
uwsgi --ini uwsgi.ini
# 停止uwsgi服務
uwsgi --stop uwsgi.pid
# 重啟uwsgi服務
uwsgi --reload uwsgi.pid
——MDI窗口 QMdiArea QMdiSubWindow)
