Flask Web 应用：文件上传、模型训练和预测

{ "title": "Flask Web 应用：文件上传、模型训练和预测", "description": "本 Flask Web 应用实现文件上传、模型训练和预测功能，并提供下载模型和结果的功能。用户可以上传 CSV 文件，训练模型并进行预测，并下载模型和预测结果。", "keywords": "Flask, 文件上传, 模型训练, 预测, CSV, 下载, 模型, 结果, web 应用", "content": ""# 导入必要的库\nfrom flask import Flask, render_template, request, redirect, url_for\nimport os\nimport pandas as pd\nfrom joblib import load\nfrom RuanJianBei.web.demo import preprocessing,data_scale,select_model,get_score\n\napp = Flask(name) \n\n# 设置上传文件的保存路径\napp.config['UPLOAD_FOLDER'] = 'uploads'\n# 设置模型保存路径\napp.config['MODEL_FOLDER'] = 'models'\n\n\n# 首页路由，显示上传表单和分类结果\n@app.route('/')\ndef index():\n return render_template('index.html')\n\n\n# 上传文件路由\n@app.route('/upload', methods=['POST'])\ndef upload():\n # 获取上传的文件\n file = request.files['file']\n # 保存文件到指定路径\n file.save(os.path.join(app.config['UPLOAD_FOLDER'], file.filename))\n\n # 加载训练数据\n data = pd.read_csv(os.path.join(app.config['UPLOAD_FOLDER'], file.filename))\n # 2.探索数据：查看数据空值情况，数据分布，数据相关性，数据特殊特征\n data = preprocessing(data)\n print(data)\n # 3.数据预处理：空值处理，值映射(分段)，归一化/标准化\n X = data.iloc[:, 0:-1]\n Y = data.iloc[:, -1]\n X = data_scale(X)\n # # 4.分割测试集和训练集\n # X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.33, random_state=42)\n # 5.选择模型\n model = select_model()\n # # 6.训练模型\n # model.fit(X_train, y_train)\n # 7.评价模型：要求用F1\n get_score()\n # 保存模型\n model.save(os.path.join(app.config['MODEL_FOLDER'], 'model.pkl'))\n return redirect(url_for('index'))\n\n\n# 下载训练模型路由\n@app.route('/download', methods=['GET'])\ndef download():\n # TODO: 在此处添加下载训练模型的代码\n return redirect(url_for('index'))\n\n\n# 测试样本上传和分类结果展示路由\n@app.route('/test', methods=['POST'])\ndef test():\n # 获取上传的文件\n file = request.files['file']\n # 保存文件到指定路径\n file.save(os.path.join(app.config['UPLOAD_FOLDER'], file.filename))\n\n # 加载测试数据\n test_data = pd.read_csv(os.path.join(app.config['UPLOAD_FOLDER'], file.filename))\n # 加载训练好的模型\n model = load(os.path.join(app.config['MODEL_FOLDER'], 'model.pkl'))\n # 使用模型进行分类\n result = model.predict(test_data)\n # 保存分类结果到文件中\n result.to_csv(os.path.join(app.config['UPLOAD_FOLDER'], 'result.csv'), index=False)\n\n return redirect(url_for('index'))\n\n\n# 下载分类结果路由\n@app.route('/download_result', methods=['GET'])\ndef download_result():\n # TODO: 在此处添加下载分类结果的代码\n return redirect(url_for('index'))\n\n\nif name == 'main':\n app.run(debug=True)\n"}