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import gradio as gr
import time
from src.pipeline import generate_report
from src.tools_loader import get_tools
# Pre-load models/tools once to avoid cold start delays
_ = get_tools()
def process_inputs(target_variable: str, image_path: str):
"""Gradio callback to generate SHAP explanation report."""
if not image_path:
return "**Please upload a SHAP summary plot image to begin.**"
if not target_variable.strip():
return "**Please enter a target variable (e.g., life expectancy).**"
start = time.time()
report = generate_report(target_variable.strip(), image_path)
elapsed = time.time() - start
return f"""### SHAP Explanation Report for **{target_variable.strip()}**
{report}
---
*Generated in {elapsed:.1f} seconds*
"""
# Gradio App Interface
with gr.Blocks(
theme=gr.themes.Soft(),
title="SHAP Summary Plot Explainer",
css="""
.input-section { max-width: 600px; margin: 0 auto; }
.report-output { margin-top: 30px; }
"""
) as demo:
# Header
gr.Markdown("# SHAP Summary Plot Explainer\n\nUpload a SHAP plot and specify your prediction target to get a detailed explanation.")
with gr.Column(elem_classes=["input-section"]):
target_input = gr.Textbox(
label="Target Variable",
placeholder="e.g., life expectancy, credit score, disease risk..."
)
shap_image = gr.Image(
type="filepath",
label="Upload SHAP Summary Plot Image",
height=350
)
generate_button = gr.Button("Generate Explanation", variant="primary")
with gr.Column(elem_classes=["report-output"]):
report_output = gr.Markdown("**Awaiting input...**")
# Link inputs to callback
generate_button.click(
fn=process_inputs,
inputs=[target_input, shap_image],
outputs=report_output,
show_progress="full"
)
if __name__ == "__main__":
demo.launch() |