app.py

import gradio as gr
from rag import rbc_product
from tool import rival_product
from graphrag import reasoning
from knowledge import graph
from pii import derisk

# Define the Google Analytics script
head = """
<!-- Google tag (gtag.js) -->
<script async src="https://www.googletagmanager.com/gtag/js?id=G-SRX9LDVBCW"></script>
<script>
  window.dataLayer = window.dataLayer || [];
  function gtag(){dataLayer.push(arguments);}
  gtag('js', new Date());

  gtag('config', 'G-SRX9LDVBCW');
</script>
"""

with gr.Blocks(head=head) as demo:
  with gr.Tab("RAG"):
    gr.Markdown("""
Links:
------------
- https://huggingface.co/spaces/kevinhug/clientX
- https://kevinwkc.github.io/davinci/
    """)


    gr.Markdown("""
    Objective: Recommend RBC product based on persona.
    ================================================
    - 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)

    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
    """)

  with gr.Tab("Tool Use"):
    gr.Markdown("""
    Objective: Recommend financial product based on persona for competitive analysis, product feature discovery
    ================================================
    - 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)

  with gr.Tab("graphrag"):
    gr.Markdown("""
    Objective: Create a Marketing Plan based on persona.
    =======================
    - Reasoning from context, answering the question
    """)

    marketing = """
A business model is not merely a static description but a dynamic ecosystem defined by five interdependent pillars:

Value Creation (What you sell): The core offering must solve a critical pain point or unlock untapped demand. This is the foundation of your value proposition—quantifiable (e.g., cost efficiency) or qualitative (e.g., exceptional user experience)—that differentiates you in the market.

Delivery Infrastructure (How you deliver): Channels and partnerships must align to ensure seamless access to your offering. For instance, a SaaS company might leverage cloud platforms for instant scalability, while a luxury brand prioritizes exclusive retail partnerships.

Customer Lifecycle Dynamics:

Acquisition: How do users discover you? Channels like organic search (SEO), targeted ads, or influencer partnerships must map to your customer segments’ behaviors.

Activation: Do first-time users experience immediate value? A fitness app, for example, might use onboarding tutorials to convert sign-ups into active users.

Retention: Is engagement sustained? Metrics like churn rate and CLV reveal whether your model fosters loyalty through features like personalized content or subscription perks.

Referral: Do users become advocates? Incentivize sharing through referral programs or viral loops (e.g., Dropbox’s storage rewards).

Revenue Architecture (How you monetize): Align pricing models (subscriptions, freemium tiers) with customer willingness-to-pay. For instance, a niche market might sustain premium pricing, while a mass-market product prioritizes volume.

Cost Symmetry: Every activity—from R&D to customer support—must balance against revenue streams. A low-cost airline, for example, optimizes for operational efficiency to maintain profitability.

Strategic Imperatives for Modern Business Models

Systemic Integration: Ohmae’s “3C’s” (Customer, Competitor, Company) remind us that acquisition channels and value propositions must adapt to shifting market realities. For instance, a retailer might pivot from brick-and-mortar to hybrid models post-pandemic.

Data-Driven Iteration: Use AARRR metrics to identify leaks in the funnel. If activation rates lag, refine onboarding; if referrals stagnate, enhance shareability.

Scalability through Partnerships: Key partners (e.g., tech vendors, logistics providers) can reduce overhead while expanding reach—critical for transitioning from niche to mass markets.

By framing each component as a strategic variable rather than a fixed element, businesses can continuously adapt to disruptions—a necessity in Ohmae’s vision of fluid, customer-first strategy.
    """
    in_verbatim = gr.Textbox(label="Context", value=marketing, visible=False)
    in_question = gr.Textbox(label="Persona")
    out_product = gr.Textbox(label="Plan")

    gr.Examples(
      [
        [
          """
Create marketing campaign that can improve customer acquisition, activation, retention and referral for this persona: 
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_question]
    )
    btn_recommend = gr.Button("Reasoning")
    btn_recommend.click(fn=reasoning, inputs=[in_verbatim, in_question], outputs=out_product)


  with gr.Tab("Knowledge Graph"):
    gr.Markdown("""
    Objective: Explain concept in knowledge graph structured output
    ================================================
    """)
    in_verbatim = gr.Textbox(label="Question")
    out_product = gr.JSON(label="Knowledge Graph")

    gr.Examples(
      [
        [
          "Explain me about red flags in transaction pattern for fraud detection"
          ]
      ],
      [in_verbatim]
    )
    btn_recommend = gr.Button("Graph It!")
    btn_recommend.click(fn=graph, inputs=in_verbatim, outputs=out_product)


  with gr.Tab("pii masking"):
    gr.Markdown("""
    Objective: pii data removal
    ================================================
    """)
    in_verbatim = gr.Textbox(label="Peronal Info")
    out_product = gr.Textbox(label="PII")

    gr.Examples(
      [
        [
        """
        He Hua (Hua Hua) Director
        [email protected]
        +86-28-83505513

        Alternative Address Format:
        Xiongmao Ave West Section, Jinniu District (listed in some records as 610016 postcode)
        """
          ]
      ],
      [in_verbatim]
    )
    btn_recommend = gr.Button("Mask PII")
    btn_recommend.click(fn=derisk, inputs=in_verbatim, outputs=out_product)

demo.launch(allowed_paths=["./"])