你好，世界，
我刚开始学习Python和Flask API，正在尝试导入一个CSV文件并导出另一个包含预测结果的CSV文件
输入的CSV文件看起来像这样
experience test_score interview five 10 110 nan 5 140 six 15 90 .....Python脚本看起来像这样（app.py）
from flask import Flask, make_response, requestimport ioimport csvapp = Flask(__name__)def transform(text_file_contents):return text_file_contents.replace("=", ",")@app.route('/')def form():return """ <html> <body> <h1>Let's TRY to Predict..</h1> </br> </br> <p> Insert your CSV file and then download the Result <form action="/transform" method="post" enctype="multipart/form-data"> <input type="file" name="data_file" class="btn btn-block"/> </br> </br> <button type="submit" class="btn btn-primary btn-block btn-large">Predict</button> </form> </body> </html>"""@app.route('/transform', methods=["POST"])def transform_view():f = request.files['data_file']if not f: return "No file"stream = io.StringIO(f.stream.read().decode("UTF8"), newline=None)csv_input = csv.reader(stream)#print("file contents: ", file_contents)#print(type(file_contents))print(csv_input)for row in csv_input: print(row)stream.seek(0)result = transform(stream.read())response = make_response(result)response.headers["Content-Disposition"] = "attachment; filename=result.csv"return responseif __name__ == "__main__":app.run(debug=True)在anaconda提示符中执行前面的脚本，代码如下
base (C:\Users\username\ python app.py上述代码将为我们提供一个HTTP URL，我们可以复制/粘贴到浏览器中
我们得到以下屏幕
因此，我能够插入一个CSV文件并下载另一个CSV文件。
我想要的是应用一个模型脚本并预测CSV中所有行的薪资。
我看过许多教程，提供了预测值的脚本，比如https://www.youtube.com/watch?v=UbCWoMf80PY。然而，在大多数情况下，我们必须插入每个值才能得到预测结果。因此，我想一次预测整个CSV文件。
模型脚本看起来像这样
df = pd.read_csv('hiring.csv')df['experience'].fillna(0, inplace=True)# Call the Get_dummies functionX = df.drop('salary', axis=1)y = df.salaryX_train, X_test, y_train, y_test = train_test_split (X, y, test_size=0.33, random_state=0) from sklearn.tree import DecisionTreeRegressortree = DecisionTreeTreeRegressor (max_depth = 4, random_state = 42)tree.fit(X_train, y_train)pred_tree = tree.predict(X_test)输出CSV应该看起来像这样
experience test_score interview salary ten 10 110 1500 nan 5 140 1870six 15 90 1650.....回答：
你首先训练模型并保存它:
import pandas as pdimport numpy as npimport pickledf = pd.read_csv('hiring.csv')df['experience'].fillna(0, inplace=True)# Call the Get_dummies functionX = df.drop('salary', axis=1)y = df.salaryX_train, X_test, y_train, y_test = train_test_split (X, y, test_size=0.33, random_state=0) from sklearn.tree import DecisionTreeRegressortree = DecisionTreeTreeRegressor (max_depth = 4, random_state = 42)tree.fit(X_train, y_train)pred_tree = tree.predict(X_test)# save the model to diskfilename = 'finalized_model.sav'pickle.dump(tree, open(filename, 'wb'))然后导入模型并对上传的文件运行预测…然后可以使用相同的应用程序下载预测结果:
from flask import Flask, make_response, requestimport iofrom io import StringIOimport csvimport pandas as pdimport numpy as npimport pickleapp = Flask(__name__)def transform(text_file_contents): return text_file_contents.replace("=", ",")@app.route('/')def form(): return """ <html> <body> <h1>Let's TRY to Predict..</h1> </br> </br> <p> Insert your CSV file and then download the Result <form action="/transform" method="post" enctype="multipart/form-data"> <input type="file" name="data_file" class="btn btn-block"/> </br> </br> <button type="submit" class="btn btn-primary btn-block btn-large">Predict</button> </form> </body> </html> """@app.route('/transform', methods=["POST"])def transform_view(): f = request.files['data_file'] if not f: return "No file" stream = io.StringIO(f.stream.read().decode("UTF8"), newline=None) csv_input = csv.reader(stream) #print("file contents: ", file_contents) #print(type(file_contents)) print(csv_input) for row in csv_input: print(row) stream.seek(0) result = transform(stream.read()) df = pd.read_csv(StringIO(result)) # load the model from disk loaded_model = pickle.load(open(filename, 'rb')) df['prediction'] = loaded_model.predict(df) response = make_response(df.to_csv()) response.headers["Content-Disposition"] = "attachment; filename=result.csv" return responseif __name__ == "__main__": app.run(debug=False,port=9000)