first commit

app.py

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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)

requirements.txt

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+hopsworks