Update app (2).py

app (2).py

@@ -18,68 +18,127 @@ def safe_convert(value, default, min_val, max_val):
     except (TypeError, ValueError):
         return default
 
-# Main function for prediction
-def main_func(Category, InventoryBucket, Product, Conference, Geography,
-              Baseball, Event, LearningDevelopment, WorkEnvironment, Engagement, WellBeing):
-    
-    category_mapping = {
-        "Food": 1,
-        "Beverage": 2,
-        "Merchandise": 3,
-        "Experiences": 4
-    }
-
+def main_func(InventoryBucket, Category, Product, Geography, Conference):
     inventory_mapping = {
-        "Low": 1,
-        "Medium": 2,
-        "High": 3
+        "Spot": 1,
+        "Field-Level Signage": 2,
+        "LED Ribbon Board Signage": 3,
+        "Season Ticket": 4,
+        "Videoboard Promotion": 5,
+        "Field or Event Access": 6,
+        "Program and Guide (Print)": 7,
+        "Rotational Signage": 8,
+        "Live Read": 9,
+        "Parking": 10,
+        "Ticket": 11,
+        "Feature": 12,
+        "Marketing Space": 13,
+        "In-Game Promotion": 14,
+        "PA Announcement": 15,
+        "Scoreboard Signage": 16,
+        "Activation": 17,
+        "Suite": 18,
+        "Billboard": 19,
+        "Entitlement/Sponsorship": 20,
+        "Game Entitlement": 21,
+        "Club Ticket": 22,
+        "Post Season Revenue": 23,
+        "Display": 24,
+        "Miscellaneous Signage": 25,
+        "Program and Guide (Digital)": 26,
+        "Concourse Signage": 27,
+        "Giveaway": 28,
+        "Tabling": 29,
+        "Game Sponsor": 30,
+        "Yearbook": 31,
+        "Live Mention": 32,
+        "Rights & Licensing": 33,
+        "Schedule": 34,
+        "Virtual Signage": 35,
+        "Venue": 36,
+        "Tailgating": 37,
+        "Trip": 38,
+        "Presenting Sponsor": 39,
+        "Concession Signage": 40,
+        "Fan Engagement Area": 41,
+        "Venue Entitlement": 42,
+        "Sideline Branding": 43,
+        "Autographed Item": 44,
+        "Post Season Ticket": 45,
+        "Poster": 46,
+        "Courtside Ticket": 47,
+        "Box Ticket": 48,
+        "Vomitory Signage": 49,
+        "Group Tickets": 50,
+        "Pre-Game Specialty Sponsor": 51,
+        "Sideline Assets": 52,
+        "Roster Card": 53,
+        "Sideline Signage": 54,
+        "Stair Sign": 55,
+        "Photo & Meet and Greet": 56,
+        "Tour": 57,
+        "On-Field Promotion": 58
     }
 
-    product_mapping = {
-        "Standard": 1,
-        "Premium": 2,
-        "Bundle": 3
+    category_mapping = {
+        "Signage": 1,
+        "Radio": 2,
+        "Tickets & Hospitality": 3,
+        "Promotion": 4,
+        "Print": 5,
+        "Activation": 6,
+        "Entitlement & Sponsorship": 7,
+        "Television": 8,
+        "Experiential": 9,
+        "Miscellaneous": 10,
+        "Intellectual Property": 11
     }
 
-    conference_mapping = {
-        "Yes": 1,
-        "No": 0
+    product_mapping = {
+        "Football": 1,
+        "Basketball - Men's": 2,
+        "Basketball - Women's": 3,
+        "Baseball": 4,
+        "Basketball": 5,
+        "Hockey": 6,
+        "Arena Sports": 7,
+        "Volleyball": 8,
+        "Lacrosse - Men's": 9,
+        "Softball": 10,
+        "Gymnastics": 11,
+        "Soccer": 12,
+        "Olympic Sports": 13,
+        "Lacrosse": 14,
+        "Soccer - Women's": 15,
+        "Lacrosse - Women's": 16,
+        "Soccer - Men's": 17,
+        "Wrestling": 18
     }
 
     geography_mapping = {
-        "North": 1,
-        "South": 2,
-        "East": 3,
-        "West": 4
+        "South": 1,
+        "East Coast": 2,
+        "Midwest": 3,
+        "Mountain West": 4,
+        "West Coast": 5
     }
 
-    # Convert inputs
-    SupportiveGM = safe_convert(SupportiveGM, 3.0, 1, 5)
-    Merit = safe_convert(Merit, 3.0, 1, 5)
-    LearningDevelopment = safe_convert(LearningDevelopment, 3.0, 1, 5)
-    WorkEnvironment = safe_convert(WorkEnvironment, 3.0, 1, 5)
-    Engagement = safe_convert(Engagement, 3.0, 1, 5)
-    WellBeing = safe_convert(WellBeing, 3.0, 1, 5)
+    conference_mapping = {
+        "Power Four": 1,
+        "Group of Five": 2,
+        "Non-Power Four / Group of Five": 3,
+        "Conference": 4
+    }
 
-    # Create input dataframe
     new_row = pd.DataFrame({
-        'Category': [category_mapping[Category]],
         'InventoryBucket': [inventory_mapping[InventoryBucket]],
+        'Category': [category_mapping[Category]],
         'Product': [product_mapping[Product]],
-        'Conference': [conference_mapping[Conference]],
         'Geography': [geography_mapping[Geography]],
-        'SupportiveGM': [SupportiveGM],
-        'Merit': [Merit],
-        'LearningDevelopment': [LearningDevelopment],
-        'WorkEnvironment': [WorkEnvironment],
-        'Engagement': [Engagement],
-        'WellBeing': [WellBeing]
+        'Conference': [conference_mapping[Conference]]
     }).astype(float)
 
-    # Predict revenue
     prediction = loaded_model.predict(new_row)[0]
-
-    # SHAP analysis
     shap_values = explainer(new_row)
 
     fig, ax = plt.subplots(figsize=(8, 4))
@@ -94,85 +153,48 @@ def main_func(Category, InventoryBucket, Product, Conference, Geography,
 
     return f"Predicted Revenue: ${prediction:,.2f}", local_plot
 
-# UI Labels
-title = "**Mod 5 Team 2: Playfly Revenue Predictor & Interpreter**"
-description1 = """
-This app predicts **revenue** based on product attributes and organizational factors.
-It considers category, inventory levels, geography,and product —to generate revenue projections.
-The app provides:
-- **Predicted Revenue Output**
-- **SHAP Waterfall Plot** to explain prediction drivers
-"""
-
-description2 = "Adjust the product and workplace inputs below and click 'Analyze' to get your revenue prediction and explanation."
-
-# Gradio UI
-with gr.Blocks(title=title) as demo:
-    gr.Markdown(f"## {title}")
-    gr.Markdown(description1)
-    gr.Markdown("""---""")
-    gr.Markdown(description2)
-    gr.Markdown("""---""")
-    
+with gr.Blocks(title="Playfly Revenue Predictor") as demo:
+    with gr.Row():
+        gr.Markdown("## Playfly Revenue Predictor & Interpreter")
+        logo = gr.Image(label="", value="logo.png", shape=(75, 75))
+
+    gr.Markdown("This app predicts **revenue** based on selected inventory, category, sport, geography, and conference.")
+    gr.Markdown("---")
+
+    label = gr.Label(label="Revenue Prediction")
+    local_plot = gr.Plot(label="SHAP Waterfall Plot")
+
     with gr.Row():
-        with gr.Column():
-            Category = gr.Radio(
-                ["Food", "Beverage", "Merchandise", "Experiences"],
-                label="Category", 
-                value="Food"
-            )
-            InventoryBucket = gr.Dropdown(
-                ["Low", "Medium", "High"],
-                label="Inventory Bucket", 
-                value="Medium"
-            )
-            Product = gr.Radio(
-                ["Standard", "Premium", "Bundle"],
-                label="Product Type", 
-                value="Standard"
-            )
-            Conference = gr.Radio(
-                ["Yes", "No"],
-                label="Part of Conference Package?", 
-                value="No"
-            )
-            Geography = gr.Dropdown(
-                ["North", "South", "East", "West"],
-                label="Geography", 
-                value="North"
-            )
-            SupportiveGM = gr.Slider(label="Supportive GM", minimum=1, maximum=5, value=4, step=0.1)
-            Merit = gr.Slider(label="Merit", minimum=1, maximum=5, value=4, step=0.1)
-            LearningDevelopment = gr.Slider(label="Learning & Development", minimum=1, maximum=5, value=4, step=0.1)
-            WorkEnvironment = gr.Slider(label="Work Environment", minimum=1, maximum=5, value=4, step=0.1)
-            Engagement = gr.Slider(label="Engagement", minimum=1, maximum=5, value=4, step=0.1)
-            WellBeing = gr.Slider(label="Well-Being", minimum=1, maximum=5, value=4, step=0.1)
-            submit_btn = gr.Button("Analyze")
-        
-        with gr.Column(visible=True, scale=1, min_width=600) as output_col:
-            label = gr.Label(label="Revenue Prediction")
-            local_plot = gr.Plot(label='SHAP Waterfall Plot')
-
-    submit_btn.click(
+        InventoryBucket = gr.Dropdown(list(inventory_mapping.keys()), label="Inventory Bucket")
+        Category = gr.Dropdown(list(category_mapping.keys()), label="Category")
+        Product = gr.Dropdown(list(product_mapping.keys()), label="Product")
+        Geography = gr.Dropdown(list(geography_mapping.keys()), label="Geography")
+        Conference = gr.Dropdown(list(conference_mapping.keys()), label="Conference")
+
+    analyze_btn = gr.Button("Analyze", elem_id="analyze_btn")
+    
+    analyze_btn.click(
         main_func,
-        [Category, InventoryBucket, Product, Conference, Geography,
-         SupportiveGM, Merit, LearningDevelopment, WorkEnvironment, Engagement, WellBeing],
-        [label, local_plot],
-        api_name="Revenue_Predictor"
+        [InventoryBucket, Category, Product, Geography, Conference],
+        [label, local_plot]
     )
 
-    gr.Markdown("### Try one of the following example scenarios:")
+    gr.Markdown("---")
+
     gr.Examples(
         [
-            ["Food", "Medium", "Standard", "No", "North", 4.1, 3.7, 3.9, 4.2, 4.4, 4.3],
-            ["Beverage", "High", "Premium", "Yes", "East", 3.8, 3.9, 4.1, 4.3, 4.5, 4.4],
-            ["Experiences", "Low", "Bundle", "No", "South", 4.3, 4.0, 4.3, 4.4, 4.5, 4.4]
+            ["Field-Level Signage", "Signage", "Football", "East Coast", "Power Four"],
+            ["Program and Guide (Digital)", "Print", "Basketball - Women's", "Midwest", "Group of Five"]
         ],
-        [Category, InventoryBucket, Product, Conference, Geography,
-         SupportiveGM, Merit, LearningDevelopment, WorkEnvironment, Engagement, WellBeing],
+        [InventoryBucket, Category, Product, Geography, Conference],
         [label, local_plot],
         main_func,
         cache_examples=True
     )
 
+    demo.load(None, None, js="""
+        document.getElementById("analyze_btn").style.backgroundColor = "#4169E1";
+        document.getElementById("analyze_btn").style.color = "white";
+    """)
+
 demo.launch()