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app.py
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app.py
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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
import pickle
# load the model from disk
trans_data = 'tranform.pkl'
clf_pkl= 'nlp_model.pkl'
cv=pickle.load(open(f'./models/{trans_data}','rb'))# counvectorizer trnaformed data
clf = pickle.load(open(f'./models/{clf_pkl}', 'rb'))#classification model file
app = Flask(__name__)
@app.route('/')
def home():
return render_template('home.html')
@app.route('/predict',methods=['POST'])
def predict():
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)