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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() | |