Created
December 15, 2018 18:10
-
-
Save susanli2016/e22caf90134c9818167d91f2b101e845 to your computer and use it in GitHub Desktop.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
from flask import Flask,render_template,url_for,request | |
import pandas as pd | |
import pickle | |
from sklearn.feature_extraction.text import CountVectorizer | |
from sklearn.naive_bayes import MultinomialNB | |
from sklearn.externals import joblib | |
app = Flask(__name__) | |
@app.route('/') | |
def home(): | |
return render_template('home.html') | |
@app.route('/predict',methods=['POST']) | |
def predict(): | |
df= pd.read_csv("spam.csv", encoding="latin-1") | |
df.drop(['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], axis=1, inplace=True) | |
# Features and Labels | |
df['label'] = df['class'].map({'ham': 0, 'spam': 1}) | |
X = df['message'] | |
y = df['label'] | |
# Extract Feature With CountVectorizer | |
cv = CountVectorizer() | |
X = cv.fit_transform(X) # Fit the Data | |
from sklearn.model_selection import train_test_split | |
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42) | |
#Naive Bayes Classifier | |
from sklearn.naive_bayes import MultinomialNB | |
clf = MultinomialNB() | |
clf.fit(X_train,y_train) | |
clf.score(X_test,y_test) | |
#Alternative Usage of Saved Model | |
# joblib.dump(clf, 'NB_spam_model.pkl') | |
# NB_spam_model = open('NB_spam_model.pkl','rb') | |
# clf = joblib.load(NB_spam_model) | |
if request.method == 'POST': | |
message = request.form['message'] | |
data = [message] | |
vect = cv.transform(data).toarray() | |
my_prediction = clf.predict(vect) | |
return render_template('result.html',prediction = my_prediction) | |
if __name__ == '__main__': | |
app.run(debug=True) |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment