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import gradio as gr
from rag import rbc_product
from tool import rival_product
with gr.Blocks() as demo:
with gr.Tab("RAG"):
gr.Markdown("""
Marketing
------------
GraphRAG: Models customer-product relationship networks for next-best-action predictions
DSPy: Optimizes cross-sell/upsell prompt variations through A/B testing
Risk & Audit
------------
GraphRAG: Maps transactional relationships into dynamic knowledge graphs to detect multi-layered fraud patterns
Tool Use: Integrates fraud detection APIs, anomaly scoring models, and regulatory compliance checkers
DSPy: Optimizes fraud explanation prompts for regulatory reporting
""")
gr.Markdown("""
Retrieval: Public RBC Product Data
Recommend: RBC Product
""")
in_verbatim = gr.Textbox(label="Verbatim")
out_product = gr.Textbox(label="Product")
gr.Examples(
[
["Low APR and great customer service. I would highly recommend if you’re looking for a great credit card company and looking to rebuild your credit. I have had my credit limit increased annually and the annual fee is very low."]
],
[in_verbatim]
)
btn_recommend=gr.Button("Recommend")
btn_recommend.click(fn=rbc_product, inputs=in_verbatim, outputs=out_product)
with gr.Tab("Tool Use"):
gr.Markdown("""
Retrieval: Public Product Data using Tavily Search
Recommend: Competition Product
""")
in_verbatim = gr.Textbox(label="Verbatim")
out_product = gr.Textbox(label="Product")
gr.Examples(
[
["Low APR and great customer service. I would highly recommend if you’re looking for a great credit card company and looking to rebuild your credit. I have had my credit limit increased annually and the annual fee is very low."]
],
[in_verbatim]
)
btn_recommend=gr.Button("Recommend")
btn_recommend.click(fn=rival_product, inputs=in_verbatim, outputs=out_product)
demo.launch(allowed_paths=["./xgb","./ts"]) |