Flask 应用集成机器学习模型进行数据分类

"# 导入必要的库\nfrom flask import Flask, render_template, request, redirect, url_for\nimport os\nimport pandas as pd\nfrom sklearn.ensemble import RandomForestClassifier\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# 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# 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 # 数据预处理\n # ...\n # 训练模型\n model = RandomForestClassifier()\n model.fit(X, y)\n # 保存模型\n model.save(os.path.join(app.config['MODEL_FOLDER'], 'model.pkl'))\n \n return redirect(url_for('index'))\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# 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_model(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# 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\nif name == 'main':\n app.run(debug=True)\n"\n