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import pandas as pd
import pickle
import numpy as np
from sklearn import tree
import gradio as gr
# Load the Random Forest CLassifier model
filename = 'model.pkl'
loaded_model = pickle.load(open(filename, 'rb'))
print(loaded_model)
def multiline(textData):
print("inp", textData)
col=["HighBP","HighChol","CholCheck","BMI","Smoker","Stroke","HeartDiseaseorAttack","PhysActivity","Fruits"
,"Veggies","HvyAlcoholConsump","AnyHealthcare","NoDocbcCost","GenHlth","MentHlth","PhysHlth","DiffWalk"
,"Sex","Age","Education","Income"]
#empty_array = []
empty_array = np.empty((0, 21), float)
for line in textData.split("\n"):
abc = list(map(float, line.split(",")));
print(abc)
empty_array = np.append(empty_array, np.array([abc]), axis=0)
print("empty_array")
print(empty_array)
ddf = pd.DataFrame(empty_array, columns=col)
print("ddf")
print(ddf)
#print(loaded_model.predict(ddf))
return ddf
def predict2(content):
multiple_records = multiline(content)
result = loaded_model.predict(multiple_records)
print(result)
return result
iface = gr.Interface(fn=predict2, inputs="text", outputs="text")
iface.launch()
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