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import gradio as gr
import matplotlib.pyplot as plt
import shap
import hopsworks
import pandas as pd
import joblib
from sklearn.pipeline import make_pipeline
df = pd.DataFrame(
[[20, 20, 30, 40]],
columns=["age", "bmi", "hba1c_level", "blood_glucose_level"],
)
# Assuming the hopsworks login and model retrieval code works as expected
project = hopsworks.login(
project="SonyaStern_Lab1",
api_key_value="c9StuuVQPoMUeXWe.jB2XeWcI8poKUN59W13MxAbMemzY7SChOnX151GtTFNhysBBUPMRuEp5IK7SE3i1",
)
mr = project.get_model_registry()
model = mr.get_model("diabetes_gan_model", version=1)
model_dir = model.download()
model = joblib.load(model_dir + "/diabetes_gan_model.pkl")
print("printing model pipeline:", model)
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)
# rf_model = model.steps[-1][1] # Load your model
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.TreeExplainer(rf_classifier)
shap_values = explainer.shap_values(transformed_df)
predicted_class = rf_classifier.predict(transformed_df)[0]
shap_values_for_predicted_class = shap_values[predicted_class]
# base_value = explainer.expected_value[1]
fig2 = plt.figure() # Create a new figure for SHAP plot
shap.waterfall_plot(
explainer.expected_value[predicted_class], shap_values_for_predicted_class[0]
)
plt.title("SHAP Waterfall Plot") # Optionally set a title for the SHAP plot
return fig1, fig2
# Generate plots once and store them
fig1, fig2 = generate_plots()
with gr.Blocks() as demo:
with gr.Row():
gr.Plot(fig1) # Display first plot in the first row
with gr.Row():
gr.Plot(fig2) # Display SHAP waterfall plot in the second row
demo.launch()
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