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from flask import Flask, render_template, url_for, request
from flask_bootstrap import Bootstrap
import pickle
import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
import joblib
app = Flask(__name__)
Bootstrap(app)
@app.route('/')
def home():
return render_template("home.html")
@app.route('/predict', methods = ['POST'])
def predict():
#return render_template("result.html")
df= pd.read_csv("data2.csv")
df_data = df[["class", "comments"]]
df_x = df_data["comments"]
df_y = df_data["class"]
corpus = df_x
cv = CountVectorizer()
X = cv.fit_transform(corpus)
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, df_y, test_size=0.3, random_state=42)
from sklearn.linear_model import LogisticRegression
clf = LogisticRegression()
clf.fit(X_train, y_train)
clf.score(X_test, y_test)
# # #load the vectorizer
# my_vectorizer = open("comment_vectorizer.pkl", "rb")
# vector = joblib.load(my_vectorizer)
# #load the model
# my_model = open("myFinalModel.pkl","rb")
# model_clf = joblib.load(my_model)
if request.method == 'POST':
comment = request.form['comment']
data = [comment]
vect = cv.transform(data).toarray()
my_prediction = clf.predict(vect)
return render_template('home.html', name = data, prediction = my_prediction, user_comment = comment)
if __name__ == '__main__':
app.run(debug = True) |