import gradio as gr
from rag import rbc_product
from tool import rival_product
from graphrag import marketingPlan
from knowledge import graph
from pii import derisk
from classify import judge

# 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("Intro"):
    gr.Markdown("""
If you're experiencing declining market share, inefficiencies in your operations, here's how I can help:
==============
Marketing & Client Experience
------------
- 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
- Explainable AI: Intuitive visualization to help stakeholder understand model risk and flaw 

Other Links:
------------
- https://huggingface.co/spaces/kevinhug/clientX
- https://kevinwkc.github.io/davinci/
      """)

  with gr.Tab("RAG"):
    gr.Markdown("""
    Objective: Recommend RBC product based on persona.
    ================================================
    - Retrieval: Public RBC Product Data
    - Recommend: RBC Product 
    
    Potential Optimization
    ------------
    BM25 reranking using keyword
    """)
    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("""
Benefits of a Product Recommender System
====================
- Increased Sales & Revenue
Personalized recommendations drive higher conversion rates and average order value.

- Enhanced Customer Experience
Tailored suggestions make the shopping journey smoother and more relevant.

- Customer Retention & Loyalty
Relevant offers encourage repeat visits and build long-term loyalty.

- Inventory Optimization
Promotes underperforming products or clears surplus stock with strategic recommendations.
    """)

  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)

    gr.Markdown("""
Benefits of a Competitor Product Recommender
=================
- Improved Customer Retention
Prevents drop-offs by offering similar alternatives when a preferred product is unavailable or suboptimal.

- Increased Conversion Rates
Captures potential lost sales by guiding customers toward viable substitutes.

- Builds Trust and Transparency
Demonstrates a customer-first approach by recommending the best-fit product—even if it’s not your own.

- Portfolio Optimization
Helps businesses learn which competitor features customers prefer, guiding product development and pricing.
    """)
  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(
      [
        [
          """
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=marketingPlan, inputs=[in_verbatim, in_question], outputs=out_product)

    gr.Markdown("""
Benefits of a Marketing Campaign Generator
===============
- Accelerated Campaign Launches
Quickly generates tailored campaigns, reducing go-to-market time from weeks to hours.

- Improved Targeting & Personalization
Uses customer data and behavior to craft messages that resonate with specific segments.
        """)

  with gr.Tab("Knowledge Graph"):
    gr.Markdown("""
Objective: Explain concept in knowledge graph structured output
=====================================
- We create query plan by breaking down into subquery
- Using those subquery to create knowledge graph
    """)
    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)

    gr.Markdown("""
Benefits of a Knowledge Graph
============
- Smarter Data Relationships
Connects siloed data across domains to create a holistic, contextual view.

- Improved Search & Discovery
Enables semantic search—understanding meaning, not just keywords.

- Enhanced Decision-Making
Surfaces hidden patterns and relationships for better analytics and insights.

- Data Reusability
Once created, knowledge graphs can be repurposed across multiple use cases (e.g., search, recommendation, fraud detection).
          """)

  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
        hehua@chengdu.com
        +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)
    gr.Markdown("""
Benefits of Entity Removal
==================
- Data Privacy & Compliance
Ensures sensitive information (names, emails, phone numbers, etc.) is anonymized to comply with GDPR, HIPAA, or other regulations.

- Improved Data Quality
Removes noise (e.g., irrelevant names or addresses) to make datasets cleaner and more usable for modeling or analysis.

- Enhanced Focus for NLP Models
Allows downstream tasks (like sentiment analysis or topic modeling) to focus on content rather than personal identifiers.
    """)


  with gr.Tab("classification"):
    gr.Markdown("""
    Objective: Classify customer feedback into product bucket
    ================================================
    - multi class classification, could have multiple label for 1 feedback
    - fix classification in this use case: online banking, card, auto finance, mortgage, insurance
    - LLM Judge to evaluate relevancy
    """)
    in_verbatim = gr.Textbox(label="Customer Feedback separate by ;")
    out_product = gr.Textbox(label="Classification & Evaluation")

    gr.Examples(
      [
        [
        """
"The online portal makes managing my mortgage payments so convenient.";
"RBC offer great mortgage for my home with competitive rate thank you";
"Low interest rate compared to other cards I’ve used. Highly recommend for responsible spenders.";
"The mobile check deposit feature saves me so much time. Banking made easy!";
"Affordable premiums with great coverage. Switched from my old provider and saved!"
        """
          ]
      ],
      [in_verbatim]
    )
    btn_recommend = gr.Button("Classify & Evaluation")
    btn_recommend.click(fn=judge, inputs=in_verbatim, outputs=out_product)
    gr.Markdown("""
Benefits of Multi Class Classification
==================
- Precision Decision-Making
Automates complex categorization tasks (e.g., loan risk tiers, transaction types) with >90% accuracy, reducing human bias.

- Operational Efficiency
Processes 10,000+ transactions/cases per minute vs. hours manually (e.g., JP Morgan’s COiN platform reduced 360k loan doc hours to seconds).

- Risk Mitigation
Proactively flags 5+ fraud types (identity theft, money laundering) with 40% fewer false positives than rule-based systems.

- Regulatory Compliance
Auto-classifies documents for FINRA/SEC audits (e.g., Morgan Stanley uses NLP to categorize 3M+ annual communications into 50+ compliance buckets).
    """)
demo.launch(allowed_paths=["./"])