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app.py
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app.py
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# Importing essential libraries
from flask import Flask, render_template, request
import pickle
import numpy as np
# Load the Random Forest CLassifier model
filename = 'model.pkl'
classifier = pickle.load(open(filename, 'rb'))
app = Flask(__name__)
@app.route('/')
def home():
return render_template('index.html')
@app.route('/predict', methods=['POST'])
def predict():
if request.method == 'POST':
preg = int(request.form['pregnancies'])
glucose = int(request.form['glucose'])
bp = int(request.form['bloodpressure'])
st = int(request.form['skinthickness'])
insulin = int(request.form['insulin'])
bmi = float(request.form['bmi'])
dpf = float(request.form['dpf'])
age = int(request.form['age'])
data = np.array([[preg, glucose, bp, st, insulin, bmi, dpf, age]])
my_prediction = classifier.predict(data)
return render_template('result.html', prediction=my_prediction)
if __name__ == '__main__':
app.run(debug=True)