free tier

app.py

@@ -65,65 +65,6 @@ Other Links:
 - https://kevinwkc.github.io/davinci/
       """)
 
-  with gr.Tab("RAG Recommender"):
-    gr.Markdown("""
-    Objective: Dynamic RBC Product Recommender: Personalize Offers Using Customer Persona Insights
-    ================================================
-    - Retrieval: Public RBC Product Data, other massive dataset: customers data
-    - Recommend: RBC Product 
-    
-    ##### free tier hosting system limitation for this use case
-    - cannot use any workable embedding model due to big size
-    - this is not functioning correctly since I just replace embedding with a random matrix.
-    - it will work under normal environment.
-        
-    ##### 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("""
-Companies pour millions into product catalogs, marketing funnels, and user acquisition—yet many still face the same challenge:
-==================
-### 📉 Pain points:
-- High bounce rates and low conversion despite heavy traffic
-- Customers struggle to find relevant products on their own
-- One-size-fits-all promotions result in wasted ad spend and poor ROI
-
-### 🧩 The real question:
-What if your product catalog could *adapt itself* to each user in real time—just like your best salesperson would?
-
-### 🎯 The customer need:
-Businesses need a way to dynamically personalize product discovery, so every customer sees the most relevant items—without manually configuring hundreds of rules.
-
-## ✅ Enter: Product Recommender Systems
-
-By analyzing behavioral data, preferences, and historical purchases, a recommender engine surfaces what each user is most likely to want—boosting engagement and revenue.
-
-### 📌 Real-world use cases:
-- **Amazon** attributes up to 35% of its revenue to its recommender system, which tailors the home page, emails, and checkout cross-sells per user.
-- **Netflix** leverages personalized content recommendations to reduce churn and increase watch time—saving the company over $1B annually in retention value.
-- **Stitch Fix** uses machine learning-powered recommendations to curate clothing boxes tailored to individual style profiles—scaling personal styling.
-
-### 💡 Business benefits:
-- Higher conversion rates through relevant discovery
-- Increased average order value (AOV) via cross-sell and upsell
-- Improved retention and lower customer acquisition cost (CAC)
-
-If your product discovery experience isn’t working as hard as your marketing budget, it’s time to make your catalog intelligent—with recommendations that convert.
-    """)
-
   with gr.Tab("Tool Use Competitive Research"):
     gr.Markdown("""
     Objective: Persona-Driven Financial Product Recommendations: Unlock Competitive Advantage & Feature Innovation
@@ -557,4 +498,64 @@ For example, Comcast reduced repeat service calls by 17% after deploying entity
     
     This approach aligns with best-in-class use cases where feedback-driven personalization drives measurable business growth
     """)
+
+  with gr.Tab("RAG Recommender"):
+    gr.Markdown("""
+    Objective: Dynamic RBC Product Recommender: Personalize Offers Using Customer Persona Insights
+    ================================================
+    - Retrieval: Public RBC Product Data, other massive dataset: customers data
+    - Recommend: RBC Product 
+
+    ##### __free tier hosting system limitation for this use case__
+    - cannot use any workable embedding model due to big size
+    - this is not functioning correctly since I just replace embedding with a random matrix.
+    - it will work under normal environment.
+
+    ##### 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("""
+Companies pour millions into product catalogs, marketing funnels, and user acquisition—yet many still face the same challenge:
+==================
+### 📉 Pain points:
+- High bounce rates and low conversion despite heavy traffic
+- Customers struggle to find relevant products on their own
+- One-size-fits-all promotions result in wasted ad spend and poor ROI
+
+### 🧩 The real question:
+What if your product catalog could *adapt itself* to each user in real time—just like your best salesperson would?
+
+### 🎯 The customer need:
+Businesses need a way to dynamically personalize product discovery, so every customer sees the most relevant items—without manually configuring hundreds of rules.
+
+## ✅ Enter: Product Recommender Systems
+
+By analyzing behavioral data, preferences, and historical purchases, a recommender engine surfaces what each user is most likely to want—boosting engagement and revenue.
+
+### 📌 Real-world use cases:
+- **Amazon** attributes up to 35% of its revenue to its recommender system, which tailors the home page, emails, and checkout cross-sells per user.
+- **Netflix** leverages personalized content recommendations to reduce churn and increase watch time—saving the company over $1B annually in retention value.
+- **Stitch Fix** uses machine learning-powered recommendations to curate clothing boxes tailored to individual style profiles—scaling personal styling.
+
+### 💡 Business benefits:
+- Higher conversion rates through relevant discovery
+- Increased average order value (AOV) via cross-sell and upsell
+- Improved retention and lower customer acquisition cost (CAC)
+
+If your product discovery experience isn’t working as hard as your marketing budget, it’s time to make your catalog intelligent—with recommendations that convert.
+    """)
+
 demo.launch(allowed_paths=["."])