Create app.py

app.py

@@ -0,0 +1,218 @@
+import gradio as gr
+import pandas as pd
+
+# Constants and styling
+CUSTOM_CSS = """
+.gradio-container {
+    max-width: 1200px !important;
+    margin: auto;
+}
+.section-header {
+    margin-top: 1rem !important;
+    margin-bottom: 1rem !important;
+    font-size: 1.2em !important;
+}
+.results-container {
+    background-color: #f8fafc;
+    padding: 1rem;
+    border-radius: 8px;
+    margin: 1rem 0;
+}
+"""
+
+# Default compliance data
+default_compliance_df = pd.DataFrame({
+    "Regulation": ["MNPI", "GLBA", "HIPAA"],
+    "Expected Violations": [1, 1, 1],
+    "Penalty": [50000, 75000, 100000],
+    "Attorney Cost": [25000, 30000, 40000]
+})
+
+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,
+                  preamble_cost_per_user, preamble_deployment, 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
+
+    # 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
+
+    # Total benefits
+    total_benefits = labor_cost_savings + revenue_increase + compliance_savings
+
+    # Calculate costs
+    total_costs_build = initial_platform_cost + (num_ai_hires * avg_salary) + ai_maintenance_cost + ai_security_cost
+
+    # Preamble costs based on deployment model
+    if preamble_deployment == "SaaS":
+        total_costs_preamble = num_employees * preamble_cost_per_user * 12
+    elif preamble_deployment == "On-Prem (Fixed $27K/mo)":
+        total_costs_preamble = 27000 * 12
+    else:  # Guardrails option
+        total_costs_preamble = estimated_api_calls * 0.005 * 12
+
+    # 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
+
+    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:
+        gr.Markdown("# 📊 Generative AI ROI Calculator", elem_classes="section-header")
+        
+        # Results section
+        with gr.Row(elem_classes="results-container"):
+            with gr.Column():
+                build_roi_box = gr.Markdown("**ROI (Building In-House):** 0%")
+                total_costs_build_box = gr.Markdown("**Total Costs:** $0")
+            with gr.Column():
+                preamble_roi_box = gr.Markdown("**ROI (Using Preamble):** 0%")
+                total_costs_preamble_box = gr.Markdown("**Total Costs:** $0")
+        
+        # Input sections using tabs for better organization
+        with gr.Tabs():
+            # Organization Details tab
+            with gr.Tab("📊 Organization Details"):
+                with gr.Row():
+                    num_employees = gr.Slider(
+                        label="Number of Employees Impacted",
+                        minimum=1, maximum=1000, value=50,
+                        info="How many employees will use the AI solution?"
+                    )
+                    hours_saved_per_week = gr.Slider(
+                        label="Hours Saved per Week per Employee",
+                        minimum=0, maximum=40, value=5,
+                        info="Estimated time savings per employee"
+                    )
+                    hourly_wage = gr.Slider(
+                        label="Average Hourly Wage ($)",
+                        minimum=10, maximum=200, value=50,
+                        info="Average employee hourly compensation"
+                    )
+
+            # Preamble Options tab
+            with gr.Tab("💼 Preamble Options"):
+                preamble_cost_per_user = gr.Slider(
+                    label="Preamble SaaS Cost per User per Month",
+                    minimum=10, maximum=200, value=50,
+                    info="Monthly cost per user for SaaS deployment"
+                )
+                preamble_deployment = gr.Radio(
+                    ["SaaS", "On-Prem (Fixed $27K/mo)", "Guardrails ($0.005 per API call)"],
+                    label="Deployment Model",
+                    info="Choose your preferred deployment option"
+                )
+                estimated_api_calls = gr.Number(
+                    label="Monthly API Calls (Guardrails)",
+                    value=10000,
+                    info="Estimated API calls per month if using Guardrails"
+                )
+
+            # Compliance tab
+            with gr.Tab("🛡️ Compliance"):
+                compliance_data = gr.Dataframe(
+                    value=default_compliance_df,
+                    headers=["Regulation", "Expected Violations", "Penalty", "Attorney Cost"],
+                    datatype="pandas",
+                    label="Compliance Data"
+                )
+
+            # Build Costs tab
+            with gr.Tab("🔨 Build Costs"):
+                initial_platform_cost = gr.Number(
+                    label="Initial Platform Development Cost ($)",
+                    value=100000,
+                    info="One-time cost to develop the platform"
+                )
+                with gr.Row():
+                    num_ai_hires = gr.Number(
+                        label="Number of AI Personnel",
+                        value=1,
+                        info="Required AI specialists"
+                    )
+                    avg_salary = gr.Number(
+                        label="Average Annual Salary ($)",
+                        value=150000,
+                        info="Average annual salary per AI hire"
+                    )
+                with gr.Row():
+                    ai_maintenance_cost = gr.Number(
+                        label="Annual Maintenance Costs ($)",
+                        value=20000,
+                        info="Yearly platform maintenance"
+                    )
+                    ai_security_cost = gr.Number(
+                        label="Security & Compliance Costs ($)",
+                        value=10000,
+                        info="Annual security and compliance expenses"
+                    )
+
+            # Additional Benefits tab
+            with gr.Tab("📈 Benefits"):
+                revenue_increase = gr.Number(
+                    label="Estimated Annual Revenue Increase ($)",
+                    value=50000,
+                    info="Projected revenue growth from AI implementation"
+                )
+
+        # Calculate button
+        calculate_button = gr.Button("Calculate ROI", variant="primary")
+
+        # Update UI function
+        def update_ui(*inputs):
+            roi_build, roi_preamble, benefits, costs_build, costs_preamble = calculate_roi(*inputs)
+            
+            build_text = f"**ROI (Building In-House):** {roi_build:,.2f}%"
+            preamble_text = f"**ROI (Using Preamble):** {roi_preamble:,.2f}%"
+            costs_build_text = f"**Total Costs:** ${costs_build:,.2f}"
+            costs_preamble_text = f"**Total Costs:** ${costs_preamble:,.2f}"
+            
+            return build_text, costs_build_text, preamble_text, costs_preamble_text
+
+        # Connect the calculate button
+        calculate_button.click(
+            update_ui,
+            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,
+                preamble_cost_per_user, preamble_deployment, estimated_api_calls,
+                compliance_data
+            ],
+            outputs=[
+                build_roi_box,
+                total_costs_build_box,
+                preamble_roi_box,
+                total_costs_preamble_box
+            ]
+        )
+
+    return roi_app
+
+# Create and launch the app
+app = create_app()
+
+if __name__ == "__main__":
+    app.launch()