基于机器学习经文预测应用程序.zip

from flask import Flask,render_template,request,url_for import pandas as pd import numpy as np # ML Packages from sklearn.feature_extraction.text import CountVectorizer from sklearn.externals import joblib # NLP from textblob import TextBlob app = Flask(__name__) @app.route('/') def index(): return render_template('index.html') @app.route('/predict',methods=["GET","POST"]) def predict(): df= pd.read_csv("data/kjvmindata.csv") # Features and Labels df_X = df.text df_Y = df.label # Vectorization corpus = df_X cv = CountVectorizer() X = cv.fit_transform(corpus) naivebayes_model = open("models/biblepredictionNV_model.pkl","rb") clf = joblib.load(naivebayes_model) if request.method == 'POST': raw_text = request.form['rawtext'] data = [raw_text] vect = cv.transform(data).toarray() my_prediction = clf.predict(vect) pred_score = clf.predict_proba(vect) nlp_text = TextBlob(raw_text) text_sentiment = nlp_text.sentiment.polarity verse_sentiment = text_sentiment return render_template('index.html',prediction=my_prediction,pred_score=pred_score,verse_sentiment=verse_sentiment,raw_text=raw_text) if __name__ == '__main__': app.run(debug=True)