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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
from flask_bootstrap import Bootstrap
import pandas as pd
import numpy as np
# ML Packages
from sklearn.feature_extraction.text import CountVectorizer
import joblib
app = Flask(__name__)
Bootstrap(app)
@app.route('/')
def index():
return render_template('index.html')
@app.route('/predict', methods=['POST'])
def predict():
df= pd.read_csv("data/bengali_names.csv")
# Features and Labels
df_X = df["Name"]
df_Y = df.sex
# Vectorization
corpus = df_X
cv = CountVectorizer()
X = cv.fit_transform(corpus)
# Loading our ML Model
decisiontree_model = open("models/decisiontreemodel.pkl","rb")
clf = joblib.load(decisiontree_model)
# Receives the input query from form
if request.method == 'POST':
namequery = request.form['namequery']
data = [namequery]
vect = cv.transform(data).toarray()
my_prediction = clf.predict(vect)
return render_template('results.html',prediction = my_prediction,name = namequery.upper())
if __name__ == '__main__':
app.run(debug=True, port=33507)