基于Flask + Bootstrap的性别分类器.zip

from flask import Flask,render_template,url_for,request from flask_bootstrap import Bootstrap import pandas as pd import numpy as np # ML Packages from sklearn.feature_extraction.text import CountVectorizer from sklearn.externals import joblib app = Flask(__name__) Bootstrap(app) @app.route('/') def index(): return render_template('index.html') @app.route('/predict', methods=['POST']) def predict(): df= pd.read_csv("data/names_dataset.csv") # Features and Labels df_X = df.name df_Y = df.sex # Vectorization corpus = df_X cv = CountVectorizer() X = cv.fit_transform(corpus) # Loading our ML Model naivebayes_model = open("models/naivebayesgendermodel.pkl","rb") clf = joblib.load(naivebayes_model) # Receives the input query from form if request.method == 'POST': namequery = request.form['namequery'] data = [namequery] vect = cv.transform(data).toarray() my_prediction = clf.predict(vect) return render_template('results.html',prediction = my_prediction,name = namequery.upper()) if __name__ == '__main__': app.run(debug=True)