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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 pickle
import numpy as np
import pandas as pd
import re
# Set the flask app
app = Flask(
__name__,
static_url_path='',
static_folder='static',
template_folder='templates'
)
# Load the model
mlModel = pickle.load(open('models/nbModel.pkl', 'rb'))
# Tfidf algorithm
tfidf = pickle.load(open('models/tfidf.pkl', 'rb'))
# CountVectorizer algorithm
countVec = pickle.load(open('models/countVectorizer.pkl', 'rb'))
# Home page
@app.route('/', methods=['GET'])
def index():
return render_template('index.html')
# App page
@app.route('/tantei', methods=['GET'])
def tantei():
return render_template('tantei.html')
# Prediction
@app.route('/predict', methods=['GET', 'POST'])
def predict():
if request.method == 'POST':
message = request.form['message']
myPrediction = mlModel.predict([message])
probability = mlModel.predict_proba([message])
maxProba = np.amax(probability)
maxProba = format(maxProba, ".2%")
print(maxProba)
return render_template('tantei.html', prediction = myPrediction, accuracy = maxProba)
# Run the app
if __name__== '__main__':
# Run the app
app.run()