app.py

import gradio as gr
import hopsworks
import joblib
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import shap
from sklearn.pipeline import make_pipeline

feature_names = ["Age", "BMI", "HbA1c", "Blood Glucose"]

project = hopsworks.login(
    project="SonyaStern_Lab1",
    api_key_value="c9StuuVQPoMUeXWe.jB2XeWcI8poKUN59W13MxAbMemzY7SChOnX151GtTFNhysBBUPMRuEp5IK7SE3i1",
)
fs = project.get_feature_store()

print("trying to dl model")
mr = project.get_model_registry()
model = mr.get_model("diabetes_model", version=1)
model_dir = model.download()
model = joblib.load(model_dir + "/diabetes_model.pkl")
print("Model downloaded")

diabetes_fg = fs.get_feature_group(name="diabetes_gan", version=1)
query = diabetes_fg.select_all()
# feature_view = fs.get_or_create_feature_view(name="diabetes",
feature_view = fs.get_or_create_feature_view(
    name="diabetes_gan",
    version=1,
    description="Read from Diabetes dataset",
    labels=["diabetes"],
    query=query,
)
diabetes_df = pd.DataFrame(diabetes_fg.read())

with gr.Blocks() as demo:
    with gr.Row():
        gr.HTML(value="<h1 style='text-align: center;'>Diabetes prediction</h1>")
    with gr.Row():
        with gr.Column():
            age_input = gr.Number(label="age")
            bmi_input = gr.Slider(10, 100, label="bmi", info="Body Mass Index")
            hba1c_input = gr.Slider(
                3.5, 9, label="hba1c_level", info="Glycated Haemoglobin"
            )
            blood_glucose_input = gr.Slider(
                80, 300, label="blood_glucose_level", info="Blood Glucose Level"
            )
            existent_info_input = gr.Radio(
                ["yes", "no", "Don't know"],
                label="Do you already know if you have diabetes? (This will not be used for the prediction)",
            )
            consent_input = gr.Checkbox(
                info="I consent that my personal data will be saved and potentially be used for the model training",
                label="accept",
            )
            btn = gr.Button("Submit")
        with gr.Column():
            with gr.Row():
                output = gr.Text(label="Model prediction")
            with gr.Row():
                mean_plot = gr.Plot()
    with gr.Row():
        with gr.Accordion("See model explanability", open=False):
            with gr.Column():
                waterfall_plot = gr.Plot()

    def submit_inputs(
        age_input,
        bmi_input,
        hba1c_input,
        blood_glucose_input,
        existent_info_input,
        consent_input,
    ):
        df = pd.DataFrame(
            [[age_input, bmi_input, hba1c_input, blood_glucose_input]],
            columns=["age", "bmi", "hba1c_level", "blood_glucose_level"],
        )
        res = model.predict(df)

        mean_for_age = diabetes_df[
            (diabetes_df["diabetes"] == 0) & (diabetes_df["age"] == age_input)
        ].mean()

        print(
            "your bmi is:", bmi_input, "the mean for ur age is :", mean_for_age["bmi"]
        )
        categories = ["BMI", "HbA1c", "Blood Level"]
        fig, ax = plt.subplots()
        bar_width = 0.35
        indices = np.arange(len(categories))
        ax.bar(
            indices,
            [
                mean_for_age.bmi,
                mean_for_age.hba1c_level,
                mean_for_age.blood_glucose_level,
            ],
            bar_width,
            label="Reference",
            color="b",
            alpha=0.7,
        )
        ax.bar(
            indices + bar_width,
            [bmi_input, hba1c_input, blood_glucose_input],
            bar_width,
            label="User",
            color="r",
            alpha=0.7,
        )
        ax.legend()
        ax.set_xlabel("Variables")
        ax.set_ylabel("Values")
        ax.set_title("Comparison with average non-diabetic values for your age")
        ax.set_xticks(indices + bar_width / 2)
        ax.set_xticklabels(categories)

        ## explainability plots
        rf_classifier = model.named_steps["randomforestclassifier"]
        transformer_pipeline = make_pipeline(
            *[
                step
                for name, step in model.named_steps.items()
                if name != "randomforestclassifier"
            ]
        )
        transformed_df = transformer_pipeline.transform(df)

        # Generate the SHAP waterfall plot for fig2
        explainer = shap.TreeExplainer(rf_classifier)
        shap_values = explainer.shap_values(
            transformed_df
        )  # Compute SHAP values directly on the DataFrame
        predicted_class = rf_classifier.predict(transformed_df)[0]
        shap_values_for_predicted_class = shap_values[predicted_class]

        # Select the SHAP values for the first instance and the positive class
        shap_explanation = shap.Explanation(
            values=shap_values_for_predicted_class[0],
            base_values=explainer.expected_value[predicted_class],
            data=transformed_df[0],
            feature_names=df.columns.tolist(),
        )

        fig2 = plt.figure(figsize=(12, 6))  # Create a new figure for SHAP plot
        plt.title("SHAP Waterfall Plot")  # Optionally set a title for the SHAP plot
        plt.tight_layout()
        shap.waterfall_plot(
            shap_explanation
        )  # Set show=False to prevent immediate display

        ## save user's data in hopsworks
        if consent_input == True:
            user_data_fg = fs.get_or_create_feature_group(
                name="user_diabetes_data",
                version=1,
                primary_key=["age", "bmi", "hba1c_level", "blood_glucose_level"],
                description="Submitted user data",
            )
            user_data_df = df.copy()
            user_data_df["diabetes"] = existent_info_input
            user_data_fg.insert(user_data_df)
            print("inserted new user data to hopsworks", user_data_df)
        return res, fig, fig2

    btn.click(
        submit_inputs,
        inputs=[
            age_input,
            bmi_input,
            hba1c_input,
            blood_glucose_input,
            existent_info_input,
            consent_input,
        ],
        outputs=[output, mean_plot, waterfall_plot],
    )

demo.launch()