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app.py
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app.py
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import numpy as np
from sklearn.preprocessing import StandardScaler
from flask import Flask, request, jsonify, render_template
import pickle
app = Flask(__name__)
# Load the model
model = pickle.load(open('water_model.pkl','rb'))
@app.route('/')
def home():
return render_template('index.html')
@app.route('/predict',methods=['POST'])
def predict():
# Get the data from the POST request.
sc=StandardScaler()
if request.method == 'POST':
ph = float(request.form['ph'])
Hardness = float(request.form['hardness'])
Solids = float(request.form['solids'])
Chloramines = float(request.form['chloramines'])
Sulfate = float(request.form['sulfate'])
Conductivity = float(request.form['conductivity'])
Organic_carbon = float(request.form['organic_carbon'])
Trihalomethanes = float(request.form['trihalomethanes'])
Turbidity = float(request.form['turbidity'])
data = np.array([[ph, Hardness, Solids, Chloramines, Sulfate, Conductivity, Organic_carbon, Trihalomethanes, Turbidity]])
# Scale data to be fed to model
data = sc.fit_transform(data)
my_prediction = model.predict(data)
return render_template('index.html', prediction=my_prediction)
if __name__ == "__main__":
app.run(debug=True)