- numpy
- scikit-learn
- paths:
- ./dtsync/RandomForestClassifi.pk1
import js
import numpy as np
import warnings
warnings.filterwarnings("ignore")
import pickle
model = pickle.load(open('RandomForestClassifi.pk1' , 'rb'))
def predict(inputs):
output = model.predict(inputs.reshape(1, -1))[0]
return int(output)
def get_input(*args, **kwargs):
input_list=[
Element('ph').element.value,
Element('hardness').element.value,
Element('solids').element.value,
Element('chloramines').element.value,
Element('sulfate').element.value,
Element('conductivity').element.value,
Element('organic_carbon').element.value,
Element('trihalomethanes').element.value,
Element('turbidity').element.value
]
inputs=np.array(input_list)
result = predict(inputs)
if result==0:
js.throw_red_alert()
#alertify.alert('Potability Prediction', 'The water with above parameters is not potable.', function(){ alertify.success('Ok'); });
#document.getElementById('op').innerHTML = "The water with above parameters is not potable."
else:
js.throw_green_alert()
#alertify.alert('Potability Prediction', 'The water with above parameters is potable.', function(){ alertify.success('Ok'); });
#document.getElementById('op').innerHTML = "The water with above parameters is potable."
Water Potability Identification
✨ Water Quality Information ✅