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app.py
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app.py
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from flask import Flask, jsonify,render_template, request
from flask_cors import CORS
import pickle
# Load the Logistic Regression model and CountVectorizer object from disk
cv = pickle.load(open('cv.pkl','rb')) ##loading cv
classifier_lr = pickle.load(open('sentiment.pkl','rb')) ##loading model
app = Flask(__name__) ## defining flask name
app.config['CORS_HEADERS'] = 'Content-Type'
app.config['SESSION_COOKIE_DOMAIN'] = False
app.config['Access-Control-Allow-Credentials'] = True
cors = CORS(app, resources={r"/*": {"origins": "*"}}, supports_credentials=True)
@app.route('/') ## home route
def home():
# return render_template('home.html') ##at home route returning home.html to show
return jsonify(message= "running"),200
@app.route('/predict',methods=['POST']) ## on post request /predict
def predict():
if request.method=='POST':
content = request.form['text'] ## requesting the content of the text field
data = [content] ## converting text into a list
vect = cv.transform(data).toarray() ## transforming the list of sentence into vecotor form
pred = classifier_lr.predict(vect) ## predicting the class(0 = negative, 1 = neutral, 2 = positive)
# return render_template('result.html',prediction=pred) ## returning result.html with prediction var value as class value(0,1)
return jsonify(prediction = int(pred[0])), 200
if __name__ == "__main__":
app.run(debug=True) ## running the flask app as debug==True