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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() | |