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import gradio as gr
import matplotlib.pyplot as plt
import shap
import hopsworks
import pandas as pd
import joblib
project = hopsworks.login(
project="SonyaStern_Lab1",
api_key_value="c9StuuVQPoMUeXWe.jB2XeWcI8poKUN59W13MxAbMemzY7SChOnX151GtTFNhysBBUPMRuEp5IK7SE3i1",
)
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")
rf_model = model.steps[-1][1] # Load your model
df = pd.DataFrame(
[[20, 20, 30, 40]],
columns=["age", "bmi", "hba1c_level", "blood_glucose_level"],
)
def generate_plots():
# Create the first plot as before
fig1, ax1 = plt.subplots()
ax1.plot([1, 2, 3], [4, 5, 6])
ax1.set_title("Plot 1")
# Generate the SHAP waterfall plot for fig2
explainer = shap.Explainer(rf_model)
shap_values = explainer.shap_values(df)[1] # Select SHAP values for class 1
shap_values_exp = shap.Explanation(
values=shap_values[0], base_values=explainer.expected_value[1]
)
ax2 = shap.plots.waterfall(
shap_values_exp, show=False
) # Get the axis for the waterfall plot
return fig1, ax2
with gr.Blocks() as demo:
with gr.Row():
gr.Plot(generate_plots()[0]) # Display first plot in the first row
with gr.Row():
_, ax2 = generate_plots()
gr.Plot(ax2) # Display SHAP waterfall plot in the second row
demo.launch()
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