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from datetime import datetime
import gradio as gr
import hopsworks
import joblib
import pandas as pd
import numpy as np
project = hopsworks.login()
fs = project.get_feature_store()
mr = project.get_model_registry()
model = mr.get_model("heart_model", version=1)
model_dir = model.download()
model = joblib.load(model_dir + "/heart_model.pkl")
preprocessing_pipeline = joblib.load(model_dir + "/preprocessing_pipeline.pkl")
print("Model downloaded")
def predict(df):
df = preprocessing_pipeline.transform(df)
prediction = model.predict(df)
return prediction[0]
def heart(heartdisease, smoking, alcoholdrinking, stroke, diffwalking, sex, agecategory, race, diabetic, physicalactivity, genhealth, asthma, kidneydisease, skincancer, mentalhealth, physicalhealth, sleeptime, bmi):
df = pd.DataFrame({
'smoking': [smoking],
'alcohol_drinking': [alcoholdrinking],
'stroke': [stroke],
'diff_walking': [diffwalking],
'sex': [sex],
'age_category': [agecategory],
'race': [race],
'diabetic': [diabetic],
'physical_activity': [physicalactivity],
'general_health': [genhealth],
'asthma': [asthma],
'kidney_disease': [kidneydisease],
'skin_cancer': [skincancer],
'b_m_i': [bmi],
'mental_health': [mentalhealth],
'physical_health': [physicalhealth],
'sleep_time': [sleeptime],
})
# Replace Unknowns with NaNs
# Feature pipeline has an imputer
df = df.replace('Unknown', np.nan)
pred = predict(df)
if heartdisease != "Unknown":
df['heart_disease'] = heartdisease
df['timestamp'] = pd.to_datetime(datetime.now())
heart_fg = fs.get_feature_group(name="heart", version=1)
heart_fg.insert(df)
# If insert fails, insert the imputed value instead of nan
if not pred:
return "We predict that you do NOT have heart disease. (But this is not medical advice!)"
else:
return "We predict that you MIGHT have heart disease. (But this is not medical advice!)"
demo = gr.Interface(
fn=heart,
title="Heart Disease Predictive Analytics",
description="Experiment with different heart configurations.",
allow_flagging="never",
inputs=[
gr.Dropdown(['Unknown', 'No', 'Yes'], label="Heart Disease (TARGET)"),
gr.Dropdown(['Unknown', 'No', 'Yes'], label="Smoking"),
gr.Dropdown(['Unknown', 'No', 'Yes'], label="Alcohol Drinking"),
gr.Dropdown(['Unknown', 'No', 'Yes'], label="Stroke"),
gr.Dropdown(['Unknown', 'No', 'Yes'], label="Diff Walking"),
gr.Dropdown(['Unknown', 'Female', 'Male'], label="Sex"),
gr.Dropdown(['Unknown', '18-24', '25-29', '30-34', '35-39', '40-44', '45-49', '50-54', '55-59', '60-64', '65-69', '70-74', '75-79', '80 or older'], label="Age Category"),
gr.Dropdown(['Unknown', 'American Indian/Alaskan Native', 'Asian', 'Black', 'Hispanic', 'Other', 'White'], label="Race"),
gr.Dropdown(['Unknown', 'No', 'No, borderline diabetes', 'Yes', 'Yes (during pregnancy)'], label="Diabetic"),
gr.Dropdown(['Unknown', 'No', 'Yes'], label="Physical Activity"),
gr.Dropdown(['Unknown', 'Poor', 'Fair', 'Good', 'Very good', 'Excellent'], label="General Health"),
gr.Dropdown(['Unknown', 'No', 'Yes'], label="Asthma"),
gr.Dropdown(['Unknown', 'No', 'Yes'], label="Kidney Disease"),
gr.Dropdown(['Unknown', 'No', 'Yes'], label="Skin Cancer"),
gr.Number(label="Mental Health", minimum=0, maximum=30),
gr.Number(label="Physical Health", minimum=0, maximum=30),
gr.Number(label="Sleep Time", minimum=1, maximum=24),
gr.Number(label="BMI"),
],
outputs="text")
demo.launch(debug=True)
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