Update app.py

app.py

@@ -2,134 +2,175 @@ import gradio as gr
 import pandas as pd
 
 PREAMBLE_COLORS = {
-    'light': '#081423',    # Light bg
-    'yellow': '#1F2C6D',   # Yellow accent
-    'blue': '#4556E4',     # Primary blue
-    'navy': '#FFC700',     # Navy
-    'dark': '#ECF0F6'      # Dark bg
+    'light': '#081423',
+    'yellow': '#1F2C6D',
+    'blue': '#4556E4',
+    'navy': '#FFC700',
+    'dark': '#ECF0F6'
 }
 
 CUSTOM_CSS = """
-.gradio-container {
-    max-width: 1200px !important;
-    margin: auto !important;
-    padding: 2rem !important;
+/* Previous CSS styles remain the same */
+.recommendation-panel {
+    background-color: rgba(69, 86, 228, 0.1);
+    border-radius: 8px;
+    padding: 1rem;
+    margin-top: 1rem;
 }
+"""
 
-/* Preamble branded elements */
-.header {
-    background-color: #081423;
-    padding: 1.5rem;
-    border-radius: 12px;
-    margin-bottom: 2rem;
-}
+def calculate_roi(
+    num_employees, hours_saved_per_week, hourly_wage,
+    initial_platform_cost, num_ai_hires, avg_salary,
+    ai_maintenance_cost, ai_security_cost,
+    revenue_increase,
+    monthly_budget, deployment_type, existing_ai, estimated_api_calls,
+    compliance_data
+):
+    # Annual labor savings
+    total_hours_saved = num_employees * hours_saved_per_week * 52
+    labor_cost_savings = total_hours_saved * hourly_wage
 
-.header h1 {
-    color: white;
-}
+    # Calculate compliance savings
+    compliance_savings = 0
+    if compliance_data is not None:
+        try:
+            if isinstance(compliance_data, pd.DataFrame):
+                df = compliance_data
+            else:
+                df = pd.DataFrame(compliance_data)
+            for index, row in df.iterrows():
+                try:
+                    violations = float(row["Expected Violations"])
+                    penalty = float(row["Penalty"])
+                    attorney = float(row["Attorney Cost"])
+                    compliance_savings += violations * (penalty + attorney)
+                except Exception:
+                    continue
+        except Exception:
+            compliance_savings = 0
 
-.results-container {
-    background: linear-gradient(145deg, #4556E4, #1F2C6D);
-    border-radius: 12px;
-    padding: 1.5rem;
-    margin-bottom: 2rem;
-    color: white;
-}
+    # Total benefits
+    total_benefits = labor_cost_savings + revenue_increase + compliance_savings
 
-.primary-button {
-    background-color: #FFC700 !important;
-    color: #081423 !important;
-}
+    # Calculate costs for building in-house
+    total_costs_build = initial_platform_cost + (num_ai_hires * avg_salary) + ai_maintenance_cost + ai_security_cost
 
-/* Solution selector styling */
-.solution-card {
-    border: 2px solid #4556E4;
-    border-radius: 8px;
-    padding: 1rem;
-    margin: 0.5rem;
-}
+    # Calculate Preamble costs based on selected options
+    if deployment_type == "On-Premises":
+        total_costs_preamble = 27000 * 12  # Fixed cost for enterprise
+    else:  # Cloud/SaaS
+        if monthly_budget <= 10000:  # Guardrails tier
+            total_costs_preamble = estimated_api_calls * 0.005 * 12
+        else:  # SaaS tier
+            total_costs_preamble = num_employees * 50 * 12  # $50 per user per month
 
-.solution-card.recommended {
-    border-color: #FFC700;
-    background-color: rgba(255, 199, 0, 0.1);
-}
-"""
+    # ROI calculations
+    roi_build = ((total_benefits - total_costs_build) / total_costs_build) * 100 if total_costs_build else 0
+    roi_preamble = ((total_benefits - total_costs_preamble) / total_costs_preamble) * 100 if total_costs_preamble else 0
 
-# Enhanced compliance data
-default_compliance_df = pd.DataFrame({
-    "Regulation": [
-        "SEC Reg S-P (Privacy)", 
-        "GLBA (Financial)",
-        "FINRA Rules",
-        "Bank Secrecy Act",
-        "Dodd-Frank Act"
-    ],
-    "Expected Violations": [1, 1, 1, 1, 1],
-    "Penalty": [100000, 100000, 150000, 200000, 250000],
-    "Attorney Cost": [50000, 45000, 75000, 100000, 125000]
-})
-
-def recommend_solution(num_employees, compliance_needs, budget_constraint):
-    if num_employees <= 50 and budget_constraint == "Low":
-        return "Guardrails"
-    elif num_employees <= 500 and budget_constraint in ["Low", "Medium"]:
-        return "SaaS"
-    else:
-        return "Enterprise"
+    return roi_build, roi_preamble, total_benefits, total_costs_build, total_costs_preamble
 
 def create_app():
     with gr.Blocks(css=CUSTOM_CSS) as roi_app:
-        # Header with Preamble branding
+        # Header
         with gr.Row(elem_classes="header"):
             gr.Markdown(
                 "# 📊 Preamble AI ROI Calculator\n"
-                "[Visit Preamble.com](https://preamble.com) to learn more"
+                "[Visit Preamble.com](https://preamble.com/) to learn more"
             )
         
         # Results section
         with gr.Row(elem_classes="results-container"):
             with gr.Column():
-                build_roi_box = gr.Markdown("### Building In-House")
+                build_roi_box = gr.Markdown(
+                    "### 🏗�� Building In-House\n**ROI:** 0%\n**Total Costs:** $0"
+                )
             with gr.Column():
-                preamble_roi_box = gr.Markdown("### Using Preamble")
+                preamble_roi_box = gr.Markdown(
+                    "### 🚀 Using Preamble\n**ROI:** 0%\n**Total Costs:** $0"
+                )
 
-        # Solution Recommendation
+        # Product Selection Section
         with gr.Row():
             with gr.Column():
-                gr.Markdown("## 🎯 Find Your Ideal Preamble Solution")
-                budget_constraint = gr.Radio(
-                    ["Low", "Medium", "High"],
-                    label="Budget Constraint",
-                    info="Select your budget level"
+                gr.Markdown("## 🎯 Select Your Preamble Solution")
+                monthly_budget = gr.Number(
+                    label="Monthly Budget ($)",
+                    value=50000,
+                    info="Enter your monthly budget for AI governance"
                 )
-                compliance_needs = gr.Checkboxgroup(
-                    ["Privacy", "Financial", "Healthcare", "Custom"],
-                    label="Compliance Requirements"
+                deployment_type = gr.Radio(
+                    ["Cloud/SaaS", "On-Premises"],
+                    label="Deployment Type",
+                    value="Cloud/SaaS",
+                    info="Choose your preferred deployment model"
                 )
-                
-                solution_recommendation = gr.Markdown(
-                    "Complete the information above to get a solution recommendation",
-                    elem_classes="solution-card"
+                existing_ai = gr.Radio(
+                    ["Yes, we have existing AI applications", "No, we need a full platform"],
+                    label="Existing AI Applications",
+                    value="No, we need a full platform",
+                    info="Tell us about your current AI implementation"
+                )
+                estimated_api_calls = gr.Number(
+                    label="Estimated Monthly API Calls",
+                    value=1000000,
+                    info="Required for Guardrails pricing calculation"
                 )
 
         # Main tabs
         with gr.Tabs():
-            # Organization Details tab
+            # Organization Details
             with gr.Tab("🏢 Organization Details"):
                 num_employees = gr.Slider(
                     label="Number of Employees",
                     minimum=1,
-                    maximum=1000,
-                    value=215
+                    maximum=5000,
+                    value=200,
+                    info="How many employees will use the solution?"
+                )
+                hours_saved_per_week = gr.Slider(
+                    label="Hours Saved per Week per Employee",
+                    minimum=0,
+                    maximum=40,
+                    value=6.3,
+                    info="Estimated time savings per employee"
+                )
+                hourly_wage = gr.Slider(
+                    label="Average Hourly Wage ($)",
+                    minimum=10,
+                    maximum=200,
+                    value=62,
+                    info="Average employee hourly compensation"
                 )
-                # ... rest of organization details ...
 
-            # Compliance tab with enhanced UI
+            # Build Costs
+            with gr.Tab("🔨 Build Costs"):
+                initial_platform_cost = gr.Number(
+                    label="Initial Platform Development Cost ($)",
+                    value=1000000
+                )
+                with gr.Row():
+                    num_ai_hires = gr.Number(
+                        label="Number of AI Personnel",
+                        value=1
+                    )
+                    avg_salary = gr.Number(
+                        label="Average Annual Salary ($)",
+                        value=200000
+                    )
+                with gr.Row():
+                    ai_maintenance_cost = gr.Number(
+                        label="Annual Maintenance Costs ($)",
+                        value=500000
+                    )
+                    ai_security_cost = gr.Number(
+                        label="Security & Compliance Costs ($)",
+                        value=250000
+                    )
+
+            # Compliance
             with gr.Tab("🛡️ Compliance"):
-                gr.Markdown("""
-                ## US Financial Compliance Requirements
-                Select applicable regulations and adjust expected violation rates
-                """)
                 compliance_data = gr.Dataframe(
                     value=default_compliance_df,
                     headers=["Regulation", "Expected Violations", "Penalty", "Attorney Cost"],
@@ -137,45 +178,91 @@ def create_app():
                     label="US Financial Regulations"
                 )
 
-            # ... other tabs ...
+            # Benefits
+            with gr.Tab("📈 Benefits"):
+                revenue_increase = gr.Number(
+                    label="Estimated Annual Revenue Increase ($)",
+                    value=50000,
+                    info="Projected revenue growth from AI implementation"
+                )
+
+        # Product Recommendation Display
+        recommendation_box = gr.Markdown(elem_classes="recommendation-panel")
 
         # Calculate button
-        calculate_button = gr.Button(
-            "Calculate ROI",
-            elem_classes="primary-button"
-        )
+        calculate_button = gr.Button("Calculate ROI", elem_classes="primary-button")
 
-        # Update functions
-        def update_solution_recommendation(employees, budget, compliance):
-            solution = recommend_solution(employees, compliance, budget)
+        def update_recommendation(monthly_budget, deployment_type, existing_ai, num_employees):
+            if deployment_type == "On-Premises":
+                return """### 🏢 Enterprise (On-Premises)
+                **Recommended Solution**: Full Enterprise Platform
+                - Complete compliance controls
+                - On-premises deployment
+                - Dedicated support team
+                - Custom integrations
+                Price: Starting at $27,000/month"""
+            
+            if existing_ai == "Yes, we have existing AI applications" and monthly_budget <= 5000:
+                return """### ⚡ Guardrails
+                **Recommended Solution**: API-First Implementation
+                - Pay-per-call pricing at $0.005/call
+                - Perfect for existing AI applications
+                - Quick API integration
+                - Flexible scaling
+                Estimated Cost: Based on API usage"""
             
-            recommendations = {
-                "Guardrails": "Recommended for smaller teams or API-first implementations",
-                "SaaS": "Perfect for mid-sized organizations needing quick deployment",
-                "Enterprise": "Ideal for large organizations with complex compliance needs"
-            }
+            return """### 🚀 SaaS Platform
+            **Recommended Solution**: Cloud Platform
+            - $50 per user per month
+            - Quick cloud deployment
+            - Full platform features
+            - Regular updates
+            Estimated Monthly Cost: ${:,.2f}""".format(num_employees * 50)
+
+        def update_results(*inputs):
+            roi_build, roi_preamble, benefits, costs_build, costs_preamble = calculate_roi(*inputs)
             
-            return f"""### Recommended Solution: {solution}
-            {recommendations[solution]}
+            build_text = (
+                f"### 🏗️ Building In-House\n"
+                f"**ROI:** {roi_build:,.1f}%\n"
+                f"**Total Costs:** ${costs_build:,.2f}\n"
+                f"**Total Benefits:** ${benefits:,.2f}"
+            )
             
-            **Key Benefits:**
-            - {'Lowest cost, pay-per-call pricing' if solution == 'Guardrails' else 'Simple per-user pricing' if solution == 'SaaS' else 'Full enterprise features'}
-            - {'API-first implementation' if solution == 'Guardrails' else 'Quick cloud deployment' if solution == 'SaaS' else 'On-premises deployment'}
-            - {'Perfect for developers' if solution == 'Guardrails' else 'Great for business users' if solution == 'SaaS' else 'Complete compliance controls'}
-            """
+            preamble_text = (
+                f"### 🚀 Using Preamble\n"
+                f"**ROI:** {roi_preamble:,.1f}%\n"
+                f"**Total Costs:** ${costs_preamble:,.2f}\n"
+                f"**Total Benefits:** ${benefits:,.2f}"
+            )
+            
+            return build_text, preamble_text
 
         # Connect the recommendation update
-        for input_elem in [num_employees, budget_constraint, compliance_needs]:
+        for input_elem in [monthly_budget, deployment_type, existing_ai, num_employees]:
             input_elem.change(
-                fn=update_solution_recommendation,
-                inputs=[num_employees, budget_constraint, compliance_needs],
-                outputs=[solution_recommendation]
+                fn=update_recommendation,
+                inputs=[monthly_budget, deployment_type, existing_ai, num_employees],
+                outputs=[recommendation_box]
             )
 
-        # ... rest of the calculation logic ...
+        # Connect the calculate button
+        calculate_button.click(
+            fn=update_results,
+            inputs=[
+                num_employees, hours_saved_per_week, hourly_wage,
+                initial_platform_cost, num_ai_hires, avg_salary,
+                ai_maintenance_cost, ai_security_cost,
+                revenue_increase,
+                monthly_budget, deployment_type, existing_ai, estimated_api_calls,
+                compliance_data
+            ],
+            outputs=[build_roi_box, preamble_roi_box]
+        )
 
     return roi_app
 
+# Create and launch the app
 app = create_app()
 
 if __name__ == "__main__":