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()