Update app.py

app.py

@@ -1,522 +1,1459 @@
 import gradio as gr
 import pandas as pd
+import numpy as np
+import plotly.graph_objects as go
 
-# Preamble brand colors - exact hex codes
 BRAND_COLORS = {
-    'dark_navy': '#081423',
-    'navy': '#1F2C6D',
-    'light_blue': '#4556E4',
-    'yellow': '#FFC700',
-    'light_gray': '#ECF0F6'
+    'primary': '#1F2C6D',  # Navy
+    'secondary': '#081423',  # Dark Navy
+    'accent': '#FFC700',  # Yellow
+    'dark_bg': '#081423',  # Dark Navy
+    'light_bg': '#ECF0F6',  # Light Gray
+    'button_bg': '#FFC700',  # Yellow
+    'light_text': '#FFFFFF',  # White
+    'dark_text': '#081423',  # Dark Navy
+    'table_header': '#4556E4',  # Light Blue
+    'table_row_odd': '#FFFFFF',  # Pure white
+    'table_row_even': '#ECF0F6',  # Light Gray
+    'label_text': '#000000',  # Black for Labels
+    'button_text': '#081423',  # Dark Navy for Button Text
+    'input_text': '#081423',  # Dark Navy for Input Text
+    'result_header': '#081423',  # Dark Navy for Result Headings
+    'result_text': '#FFFFFF',  # White
+    'result_bg': '#081423'  # Dark Navy
 }
 
+default_departments = pd.DataFrame({
+    "Department": ["IT", "Finance", "HR", "Marketing", "Sales", "Operations"],
+    "Number of Employees": [15, 10, 8, 12, 20, 25],
+    "Average Salary": [95000, 85000, 70000, 75000, 85000, 65000],
+    "Hours Per Week on Manual Tasks": [20, 15, 12, 10, 8, 18]
+})
 
-default_compliance_df = pd.DataFrame({
-    "Regulation": [
-        "SEC Regulation S-P (Privacy)",
-        "GLBA (Gramm-Leach-Bliley Act)",
-        "FINRA Rule 2210 (Communications)",
-        "SEC Rule 17a-4 (Records Retention)",
-        "BSA/AML Compliance",
-        "Regulation S-ID (Identity Theft Red Flags)"
-    ],
-    "Expected Violations": [1, 1, 1, 1, 1, 1],
-    "Penalty": [
-        1000000,    # SEC Reg S-P: Up to $1M per violation
-        100000,     # GLBA: Up to $100K per violation
-        250000,     # FINRA Rule 2210: Up to $250K per violation
-        500000,     # SEC 17a-4: Up to $500K per violation
-        25000,      # BSA/AML: $25K per day
-        3640,       # Reg S-ID: $3,640 per violation (2024 adjusted)
+default_subscriptions = pd.DataFrame({
+    "Tool Category": [
+        "CRM", "ERP", "Project Management", "Cloud Storage",
+        "SaaS Subscriptions"
     ],
-    "Attorney Cost": [
-        75000,  # Complex privacy cases
-        50000,  # GLBA compliance
-        40000,  # Communications review
-        60000,  # Records management
-        80000,  # AML program review
-        35000   # Identity theft program
-    ]
+    "Current Monthly Cost per User": [80, 150, 30, 25, 45],
+    "Number of Users": [90, 90, 90, 90, 90],
+    "Estimated Reduction %": [30, 20, 50, 10, 15]
 })
-CUSTOM_CSS = """
-.gradio-container {
-    max-width: 1200px !important;
-    margin: auto !important;
-    padding: 2rem !important;
-    background-color: #081423 !important;
-    font-family: 'Inter', sans-serif !important;
-}
 
-/* Main container */
-.main-container {
-    display: flex !important;
-    flex-direction: column !important;
-    gap: 2rem !important;
+industry_compliance_data = {
+    "Financial Services":
+    pd.DataFrame({
+        "Regulation": ["GDPR", "CCPA", "SOX", "PCI DSS", "GLBA"],
+        "Potential Violations": [2, 1, 1, 3, 2],
+        "Penalty": [250000, 150000, 100000, 200000, 175000],
+        "Attorney Cost": [50000, 40000, 30000, 45000, 40000]
+    }),
+    "Healthcare":
+    pd.DataFrame({
+        "Regulation":
+        ["HIPAA", "HITECH Act", "GDPR", "CCPA", "Data Protection Act 2018"],
+        "Potential Violations": [3, 2, 1, 1, 2],
+        "Penalty": [175000, 125000, 100000, 90000, 80000],
+        "Attorney Cost": [45000, 35000, 30000, 25000, 20000]
+    }),
+    "Manufacturing":
+    pd.DataFrame({
+        "Regulation": [
+            "OSHA Regulations", "EPA Regulations", "ISO Standards",
+            "NIST Cybersecurity Framework", "GDPR"
+        ],
+        "Potential Violations": [2, 3, 1, 2, 1],
+        "Penalty": [75000, 100000, 50000, 150000, 200000],
+        "Attorney Cost": [20000, 30000, 15000, 45000, 50000]
+    }),
+    "Retail":
+    pd.DataFrame({
+        "Regulation": [
+            "PCI DSS", "CCPA", "GDPR", "Consumer Rights Act",
+            "Sales Tax Regulations"
+        ],
+        "Potential Violations": [3, 2, 2, 1, 3],
+        "Penalty": [100000, 125000, 150000, 50000, 75000],
+        "Attorney Cost": [30000, 40000, 50000, 15000, 25000]
+    }),
+    "Technology":
+    pd.DataFrame({
+        "Regulation": [
+            "GDPR", "CCPA", "COPPA", "Software Licensing Compliance",
+            "Export Controls"
+        ],
+        "Potential Violations": [2, 2, 1, 3, 2],
+        "Penalty": [200000, 150000, 100000, 175000, 125000],
+        "Attorney Cost": [50000, 45000, 30000, 40000, 35000]
+    }),
+    "Government/Defense":
+    pd.DataFrame({
+        "Regulation": ["FedRAMP", "CMMC", "NIST SP 800-171", "FISMA", "ITAR"],
+        "Potential Violations": [1, 2, 3, 2, 1],
+        "Penalty": [250000, 300000, 200000, 150000, 275000],
+        "Attorney Cost": [60000, 75000, 50000, 40000, 65000]
+    }),
+    "Professional Services":
+    pd.DataFrame({
+        "Regulation": [
+            "Client Confidentiality Agreements", "Professional Standards",
+            "GDPR", "CCPA", "Ethics Rules"
+        ],
+        "Potential Violations": [2, 1, 1, 1, 3],
+        "Penalty": [50000, 100000, 150000, 125000, 75000],
+        "Attorney Cost": [20000, 30000, 40000, 35000, 25000]
+    }),
+    "Energy":
+    pd.DataFrame({
+        "Regulation": [
+            "NERC CIP", "FERC Regulations", "EPA Regulations",
+            "State Public Utility Commissions", "Cybersecurity Regulations"
+        ],
+        "Potential Violations": [3, 2, 2, 1, 3],
+        "Penalty": [200000, 150000, 175000, 100000, 125000],
+        "Attorney Cost": [50000, 40000, 45000, 30000, 35000]
+    }),
+    "Other":
+    pd.DataFrame({
+        "Regulation": [
+            "Data Privacy", "Financial Reporting",
+            "Industry-Specific Requirements", "Records Retention",
+            "Security Controls"
+        ],
+        "Potential Violations": [2, 1, 1, 3, 2],
+        "Penalty": [250000, 100000, 75000, 50000, 125000],
+        "Attorney Cost": [50000, 30000, 25000, 15000, 40000]
+    })
 }
 
-/* Product selector */
-.product-selector {
-    background-color: #1F2C6D !important;
-    border-radius: 16px !important;
-    padding: 2.5rem !important;
+industry_ai_risks = {
+    "Financial Services": [
+        "Data privacy breaches with sensitive financial information",
+        "Algorithmic bias in credit decisions",
+        "Compliance violations with regulatory frameworks (e.g., GDPR, CCPA, SOX, PCI DSS, GLBA)"
+    ],
+    "Healthcare": [
+        "Patient data confidentiality breaches", "HIPAA compliance violations",
+        "Diagnostic errors with clinical decision support",
+        "Compliance with HITECH Act, GDPR, CCPA, Data Protection Act 2018"
+    ],
+    "Manufacturing": [
+        "Quality control failures from AI systems",
+        "Supply chain vulnerability from automated decisions",
+        "Safety risks from AI-controlled equipment",
+        "Compliance with OSHA, EPA, ISO standards, NIST, GDPR."
+    ],
+    "Retail": [
+        "Customer data privacy breaches", "Payment processing vulnerabilities",
+        "Biased pricing algorithms",
+        "Compliance with PCI DSS, CCPA, GDPR, Consumer Rights Act, Sales Tax regulations."
+    ],
+    "Technology": [
+        "Code vulnerabilities in AI-generated software",
+        "Intellectual property theft via AI systems",
+        "Data leakage through AI pipelines",
+        "Compliance with GDPR, CCPA, COPPA, Software Licensing, Export Controls."
+    ],
+    "Government/Defense": [
+        "Classified information exposure through AI systems",
+        "Supply chain risks in AI components",
+        "Compliance violations with FedRAMP, CMMC, NIST, FISMA, ITAR."
+    ],
+    "Professional Services": [
+        "Client confidentiality breaches",
+        "Legal privilege violations through AI tools",
+        "Inaccurate professional advice from AI systems",
+        "Compliance with Client Agreements, GDPR, CCPA, and Ethics Rules"
+    ],
+    "Energy": [
+        "Critical infrastructure vulnerability",
+        "Physical safety risks from automated control systems",
+        "Energy supply disruption from AI forecasting errors",
+        "Compliance with NERC CIP, FERC and EPA Regulations."
+    ],
+    "Other": [
+        "Data privacy and security risks", "Regulatory compliance issues",
+        "Intellectual property protection"
+    ]
 }
 
-.selector-title {
-    font-size: 2rem !important;
-    color: #ECF0F6 !important;
-    font-weight: 600 !important;
-    margin-bottom: 2.5rem !important;
-    line-height: 1.2 !important;
+department_use_cases = {
+    "IT": [
+        "Automated ticket resolution", "Infrastructure optimization",
+        "Code generation"
+    ],
+    "Finance":
+    ["Automated reporting", "Fraud detection", "Invoice processing"],
+    "HR": ["Resume screening", "Employee onboarding", "HR analytics"],
+    "Marketing":
+    ["Content generation", "Campaign optimization", "Customer segmentation"],
+    "Sales":
+    ["Lead prioritization", "Sales forecasting", "Automated follow-ups"],
+    "Operations":
+    ["Process automation", "Supply chain optimization", "Quality control"],
+    "Legal/Compliance":
+    ["Contract review", "Regulatory tracking", "Compliance monitoring"],
+    "Customer Service":
+    ["Response generation", "Ticket categorization", "Sentiment analysis"],
+    "Research": ["Literature review", "Patent analysis", "Experiment design"],
+    "Other": ["Document processing", "Data analysis", "Process automation"]
 }
 
-/* Radio sections */
-.section-title {
-    color: #ECF0F6 !important;
-    font-size: 1.1rem !important;
-    font-weight: 500 !important;
-    margin: 1.5rem 0 1rem !important;
-}
+# --- Calculation and Recommendation Functions ---
 
-.radio-container {
-    background-color: rgba(8, 20, 35, 0.3) !important;
-    border-radius: 12px !important;
-    padding: 1rem !important;
-    margin-bottom: 1.5rem !important;
-}
 
-.radio-group label {
-    background-color: rgba(8, 20, 35, 0.5) !important;
-    border: 1px solid rgba(69, 86, 228, 0.5) !important;
-    border-radius: 8px !important;
-    padding: 1rem 1.5rem !important;
-    margin: 0.5rem !important;
-    color: #ECF0F6 !important;
-    transition: all 0.2s !important;
-    font-size: 1rem !important;
-}
+def create_subscription_savings_chart(subscription_data):
+    categories = subscription_data["Tool Category"].tolist()
+    current_costs = []
+    savings = []
 
-.radio-group label.selected {
-    background-color: #4556E4 !important;
-    border-color: #4556E4 !important;
-}
+    for _, row in subscription_data.iterrows():
+        monthly_cost = row["Current Monthly Cost per User"] * row[
+            "Number of Users"]
+        reduction = row["Estimated Reduction %"] / 100
+        current_costs.append(monthly_cost)
+        savings.append(monthly_cost * reduction)
 
-/* Input fields */
-.input-row {
-    display: grid !important;
-    grid-template-columns: 1fr 1fr !important;
-    gap: 1.5rem !important;
-    margin: 1.5rem 0 !important;
-}
+    fig = go.Figure()
+    fig.add_trace(
+        go.Bar(x=categories,
+               y=current_costs,
+               name="Current Monthly Cost",
+               marker_color=BRAND_COLORS['primary'],
+               opacity=0.9))
+    fig.add_trace(
+        go.Bar(x=categories,
+               y=savings,
+               name="Potential Monthly Savings",
+               marker_color=BRAND_COLORS['accent'],
+               opacity=0.9))
+    fig.update_layout(
+        title="Potential Monthly Subscription Savings with AI",
+        barmode='group',
+        xaxis_title="Subscription Categories",
+        yaxis_title="Cost ($)",
+        legend=dict(orientation="h",
+                    yanchor="bottom",
+                    y=1.02,
+                    xanchor="center",
+                    x=0.5,
+                    font=dict(color=BRAND_COLORS['light_text'])),  # white text
+        template="plotly_white",
+        font=dict(family="Inter, sans-serif",
+                  size=12,
+                  color=BRAND_COLORS['light_text']),  # white text
+        paper_bgcolor=BRAND_COLORS['dark_bg'],
+        plot_bgcolor=BRAND_COLORS['dark_bg'],
+        margin=dict(l=40, r=40, t=80, b=40)  # black bg
+    )
+    return fig
 
-.number-input {
-    background-color: rgba(8, 20, 35, 0.3) !important;
-    border: 1px solid rgba(69, 86, 228, 0.5) !important;
-    border-radius: 8px !important;
-    padding: 1rem !important;
-    color: #ECF0F6 !important;
-}
 
-.number-input label {
-    color: #ECF0F6 !important;
-    opacity: 0.9 !important;
-    margin-bottom: 0.5rem !important;
-}
+def calculate_department_roi(dept_data, hourly_wage_multiplier=1.5):
+    results = []
+    for _, row in dept_data.iterrows():
+        department = row["Department"]
+        employees = row["Number of Employees"]
+        avg_salary = row["Average Salary"]
+        manual_hours = row["Hours Per Week on Manual Tasks"]
+        hourly_wage = avg_salary / (52 * 40) * hourly_wage_multiplier
+        efficiency_gain = np.random.uniform(0.3, 0.4)
+        annual_hours_saved = manual_hours * efficiency_gain * 52 * employees
+        annual_savings = annual_hours_saved * hourly_wage
+        implementation_cost = avg_salary * 0.2 * employees
+        roi = ((annual_savings - implementation_cost) /
+               implementation_cost) * 100 if implementation_cost > 0 else 0
+        use_cases = department_use_cases.get(
+            department,
+            department_use_cases.get(
+                next((k for k in department_use_cases
+                      if k.lower() in department.lower()), "Other")))
+        results.append({
+            "Department": department,
+            "Employees": employees,
+            "Annual Hours Saved": annual_hours_saved,
+            "Annual Cost Savings": annual_savings,
+            "Implementation Cost": implementation_cost,
+            "ROI": roi,
+            "Use Cases": use_cases[:3]
+        })
+    return sorted(results, key=lambda x: x["ROI"], reverse=True)
 
-/* Results section */
-.results-container {
-    background-color: #4556E4 !important;
-    border-radius: 16px !important;
-    padding: 2rem !important;
-    margin-top: 2rem !important;
-}
 
-.result-card {
-    background-color: #ECF0F6 !important;
-    border-radius: 12px !important;
-    padding: 2rem !important;
-    margin: 0.5rem !important;
-}
+def calculate_subscription_savings(subscription_data):
+    total_current_cost = subscription_data.apply(lambda row: row[
+        "Current Monthly Cost per User"] * row["Number of Users"] * 12,
+                                                 axis=1).sum()
+    total_savings = subscription_data.apply(
+        lambda row: row["Current Monthly Cost per User"] * row[
+            "Number of Users"] * 12 * row["Estimated Reduction %"] / 100,
+        axis=1).sum()
+    savings_percentage = (total_savings / total_current_cost
+                          ) * 100 if total_current_cost > 0 else 0
+    return {
+        "total_current_cost": total_current_cost,
+        "total_savings": total_savings,
+        "savings_percentage": savings_percentage
+    }
 
-.result-card h3 {
-    font-size: 1.5rem !important;
-    margin-bottom: 1rem !important;
-    color: #081423 !important;
-    font-weight: 600 !important;
-}
 
-.result-card p, .result-card strong {
-    color: #081423 !important;
-    font-size: 1.1rem !important;
-}
+def calculate_compliance_savings(compliance_data):
+    if compliance_data.empty:
+        return {"total_savings": 0, "key_regulations": []}
 
-/* Calculator section */
-.calculator-tabs {
-    background-color: #1F2C6D !important;
-    border-radius: 16px !important;
-    padding: 2rem !important;
-    margin-top: 2rem !important;
-}
+    compliance_data["Potential Violations"] = pd.to_numeric(
+        compliance_data["Potential Violations"], errors='coerce').fillna(0)
+    compliance_data["Penalty"] = pd.to_numeric(compliance_data["Penalty"],
+                                               errors='coerce').fillna(0)
+    compliance_data["Attorney Cost"] = pd.to_numeric(
+        compliance_data["Attorney Cost"], errors='coerce').fillna(0)
 
-.tab-nav {
-    border-bottom: 1px solid rgba(69, 86, 228, 0.3) !important;
-    padding: 0 !important;
-    margin-bottom: 2rem !important;
-}
+    total_savings = (
+        compliance_data["Potential Violations"] *
+        (compliance_data["Penalty"] + compliance_data["Attorney Cost"])).sum()
+    compliance_data['TotalCost'] = compliance_data["Potential Violations"] * (
+        compliance_data["Penalty"] + compliance_data["Attorney Cost"])
+    sorted_compliance = compliance_data.sort_values(by='TotalCost',
+                                                    ascending=False)
+    key_regulations = [{
+        "name": row["Regulation"],
+        "cost": row["TotalCost"]
+    } for _, row in sorted_compliance.head(3).iterrows()]
+    return {"total_savings": total_savings, "key_regulations": key_regulations}
 
-.tab-nav button {
-    color: #ECF0F6 !important;
-    padding: 1rem 2rem !important;
-    border-radius: 8px 8px 0 0 !important;
-    opacity: 0.7 !important;
-}
 
-.tab-nav button.selected {
-    background-color: #4556E4 !important;
-    opacity: 1 !important;
-}
+def recommend_preamble_solution(industry,
+                                org_size,
+                                monthly_budget,
+                                has_ai_app,
+                                api_calls=10000):
+    """Recommends Preamble solution with budget and AI app considerations."""
+    high_risk_industries = [
+        "Financial Services", "Healthcare", "Government/Defense"
+    ]
+    org_size = int(org_size) if org_size is not None else 0
+    monthly_budget = float(
+        monthly_budget) if monthly_budget is not None else 0.0
+    api_calls = int(api_calls) if api_calls is not None else 10000
+    shadow_ai_blocker = "[Shadow AI Blocker Chrome Extension](https://chromewebstore.google.com/detail/shadow-ai-blocker/jkcnihggbeejafmbgohlahlighagphon)"
 
-/* Sliders and inputs in calculator */
-.calculator-tabs label span:first-child {
-    color: #ECF0F6 !important;
-    font-weight: 500 !important;
-}
+    if monthly_budget < 27000:
+        if has_ai_app:
+            solution = "Guardrails Only"
+            monthly_cost = api_calls * 0.005
+            description = f"""### ⚡ Guardrails Only
+            Perfect for integrating with your existing AI application:
+            - Quick API integration.
+            - Pre-built security rules.
+            - Basic compliance templates.
+            - Agentless operation.
+            - Free Trial available.
+            - {shadow_ai_blocker}
+            $0.005 per API call (Est. monthly cost: ${monthly_cost:,.2f} at {api_calls:,} calls/month)"""
+            image = "🛡️"
+        else:
+            solution = "SMB"
+            description = f"""### 🚀 SMB
+            Perfect for teams getting started with AI:
+            - Full platform features.
+            - Standard compliance templates.
+            - Protection for up to 25 AI applications.
+            - Secure Knowledge Base.
+            - SaaS Hosting.
+            - {shadow_ai_blocker}
+            $50 per user/month"""
+            image = "🏢"
+    elif org_size >= 1000 or industry in high_risk_industries:
+        solution = "Enterprise"
+        description = f"""### 🏢 Enterprise License
+        Perfect for large or high-risk organizations:
+        - Complete compliance controls.
+        - Custom security model training.
+        - Dedicated support team.
+        - Unlimited AI system protection.
+        - Private cloud/on-premise options.
+        - {shadow_ai_blocker}
+        Starting at $27,000/month with discounts for multi-year deals"""
+        image = "🚀"
+    elif org_size >= 100:  
+        solution = "SMB"
+        description = f"""### 🚀 SMB
+        Perfect for teams getting started with AI:
+        - Full platform features.
+        - Standard compliance templates.
+        - Protection for up to 25 AI applications.
+        - Secure Knowledge Base.
+        - SaaS Hosting.
+        - {shadow_ai_blocker}
+        $50 per user/month"""
+        image = "🏢"
 
-.calculator-tabs .info-text {
-    color: #ECF0F6 !important;
-    opacity: 0.8 !important;
-}
+    else:  
+        solution = "Guardrails Only"
+        monthly_cost = api_calls * 0.005
+        description = f"""### ⚡ Guardrails Only
+            Perfect for integrating with your existing AI application:
+            - Quick API integration.
+            - Pre-built security rules.
+            - Basic compliance templates.
+            - Agentless operation.
+            - Free Trial available.
+            - {shadow_ai_blocker}
+            $0.005 per API call (Est. monthly cost: ${monthly_cost:,.2f} at {api_calls:,} calls/month)"""
+        image = "🛡️"
+    return {"solution": solution, "description": description, "image": image}
 
-/* Recommendation panel */
-.recommendation-panel {
-    background-color: #FFC700 !important;
-    border-radius: 12px !important;
-    padding: 2rem !important;
-    margin-top: 1.5rem !important;
-}
 
-.recommendation-panel h3 {
-    color: #081423 !important;
-    font-size: 1.5rem !important;
-    font-weight: 600 !important;
-    margin-bottom: 1rem !important;
-}
+def calculate_build_vs_buy_comparison(initial_dev_cost=1000000, num_ai_personnel=1, avg_annual_salary=200000, annual_maintenance=500000, security_compliance=250000):
 
-.recommendation-panel ul {
-    list-style-type: none !important;
-    padding-left: 0 !important;
-    margin: 1rem 0 !important;
-}
+    first_year_cost = initial_dev_cost + (num_ai_personnel * avg_annual_salary) + security_compliance
+    ongoing_annual_cost = (num_ai_personnel * avg_annual_salary) + annual_maintenance + security_compliance
+
+
+    three_year_build_cost = first_year_cost + (ongoing_annual_cost * 2)
+
+
+    preamble_annual_cost = 27000 * 12
+    three_year_preamble_cost = preamble_annual_cost * 3
+
+
+    three_year_savings = three_year_build_cost - three_year_preamble_cost
+    savings_percentage = (three_year_savings / three_year_build_cost) * 100
+
+    return {
+        "first_year_build": first_year_cost,
+        "ongoing_annual_build": ongoing_annual_cost,
+        "three_year_build": three_year_build_cost,
+        "annual_preamble": preamble_annual_cost,
+        "three_year_preamble": three_year_preamble_cost,
+        "three_year_savings": three_year_savings,
+        "savings_percentage": savings_percentage
+    }
+
+def create_build_vs_buy_chart(comparison_data):
+    labels = ["Year 1", "Year 2", "Year 3", "3-Year Total"]
+
+
+    build_costs = [
+        comparison_data["first_year_build"],
+        comparison_data["ongoing_annual_build"],
+        comparison_data["ongoing_annual_build"],
+        comparison_data["three_year_build"]
+    ]
+
+
+    preamble_costs = [
+        comparison_data["annual_preamble"],
+        comparison_data["annual_preamble"],
+        comparison_data["annual_preamble"],
+        comparison_data["three_year_preamble"]
+    ]
+
+    fig = go.Figure()
+
+    fig.add_trace(go.Bar(
+        x=labels,
+        y=build_costs,
+        name="Build In-House",
+        marker_color=BRAND_COLORS['primary'],
+        opacity=0.9
+    ))
+
+    fig.add_trace(go.Bar(
+        x=labels,
+        y=preamble_costs,
+        name="Preamble Enterprise",
+        marker_color=BRAND_COLORS['accent'],
+        opacity=0.9
+    ))
+
+    fig.update_layout(
+        title="Build vs Buy: 3-Year Cost Comparison",
+        barmode='group',
+        xaxis_title="Timeline",
+        yaxis_title="Cost ($)",
+        legend=dict(
+            orientation="h",
+            yanchor="bottom",
+            y=1.02,
+            xanchor="center",
+            x=0.5,
+            font=dict(color=BRAND_COLORS['light_text'])
+        ),
+        template="plotly_white",
+        font=dict(
+            family="Inter, sans-serif",
+            size=12,
+            color=BRAND_COLORS['light_text']
+        ),
+        paper_bgcolor=BRAND_COLORS['dark_bg'],
+        plot_bgcolor=BRAND_COLORS['dark_bg'],
+        margin=dict(l=40, r=40, t=80, b=40)
+    )
+
+    return fig
+
+def generate_recommendations(industry,
+                             org_size,
+                             monthly_budget,
+                             has_ai_app,
+                             dept_roi_results,
+                             subscription_savings,
+                             compliance_savings,
+                             api_calls=10000,
+                             initial_dev_cost=1000000, 
+                             num_ai_personnel=1, 
+                             avg_annual_salary=200000, 
+                             annual_maintenance=500000, 
+                             security_compliance=250000):
+    top_departments = dept_roi_results[:3]
+    solution_rec = recommend_preamble_solution(industry, org_size,
+                                               monthly_budget, has_ai_app,
+                                               api_calls)
+    total_dept_savings = sum(dept["Annual Cost Savings"]
+                             for dept in dept_roi_results)
+    industry_risks = industry_ai_risks.get(industry,
+                                           industry_ai_risks["Other"])
 
-.recommendation-panel li {
-    color: #081423 !important;
+
+    comparison = calculate_build_vs_buy_comparison(
+        initial_dev_cost, 
+        num_ai_personnel, 
+        avg_annual_salary, 
+        annual_maintenance, 
+        security_compliance
+    )
+
+    dept_recommendations = "".join(
+        f"\n    {i+1}. **{dept['Department']}** (ROI: {dept['ROI']:.1f}%)"
+        f"\n       - Annual cost savings: ${dept['Annual Cost Savings']:,.2f}"
+        f"\n       - Hours saved annually: {dept['Annual Hours Saved']:,.0f}"
+        f"\n       - Top AI use cases:\n        - " +
+        "\n        - ".join(dept["Use Cases"]) + "\n"
+        for i, dept in enumerate(top_departments))
+
+    security_text = "\n\n### Key Security Considerations\n" + "\n".join(
+        f"- {risk}" for risk in industry_risks)
+
+    subscription_text = (
+        f"\n### Subscription & Tool Optimization\n- **Current annual spend:** ${subscription_savings['total_current_cost']:,.2f}\n"
+        f"- **Potential annual savings:** ${subscription_savings['total_savings']:,.2f} ({subscription_savings['savings_percentage']:.1f}%)\n"
+        f"- **Recommendation:** Evaluate consolidation of overlapping tools with AI capabilities\n"
+    )
+
+    compliance_text = (
+        f"\n### Compliance Risk Reduction\n- **Potential risk exposure reduction:** ${compliance_savings['total_savings']:,.2f}\n"
+        f"- **Key regulations to address:**\n" +
+        "".join(f"  - {reg['name']}: ${reg['cost']:,.2f} risk exposure\n"
+                for reg in compliance_savings['key_regulations']))
+
+
+    build_vs_buy_text = (
+        f"\n### Build vs Buy Analysis\n"
+        f"- **Building in-house:**\n"
+        f"  - First year cost: ${comparison['first_year_build']:,.2f}\n"
+        f"  - Ongoing annual cost: ${comparison['ongoing_annual_build']:,.2f}\n"
+        f"  - 3-year total cost: ${comparison['three_year_build']:,.2f}\n\n"
+        f"- **Preamble Enterprise:**\n"
+        f"  - Annual cost: ${comparison['annual_preamble']:,.2f}\n"
+        f"  - 3-year total cost: ${comparison['three_year_preamble']:,.2f}\n\n"
+        f"- **3-year savings with Preamble:** ${comparison['three_year_savings']:,.2f} ({comparison['savings_percentage']:.1f}%)\n"
+        f"- **ROI:** {((comparison['three_year_savings'] / comparison['three_year_preamble']) * 100):,.1f}%\n"
+    )
+
+    report = (
+        f"# AI Implementation & Security Recommendation\n## Executive Summary\n"
+        f"**Industry:** {industry}\n**Organization Size:** {org_size:,} employees\n"
+        f"**Monthly Budget:** ${monthly_budget:,.2f}\n"
+        f"**Existing AI Application:** {'Yes' if has_ai_app else 'No'}\n"
+        f"**Total Potential Annual Savings:** ${total_dept_savings + subscription_savings['total_savings'] + compliance_savings['total_savings']:,.2f}\n"
+        f"### Recommended Preamble Solution\n{solution_rec['image']} {solution_rec['description']}\n"
+        f"{build_vs_buy_text}\n"
+        f"## Department Recommendations\nThe following departments show the highest potential ROI for AI implementation:\n{dept_recommendations}"
+        f"{subscription_text}{compliance_text}{security_text}\n## Implementation Roadmap\n\n"
+        f"1. **Phase 1: Security Foundation** (1-2 months)\n"
+        f"   - Deploy Preamble {solution_rec['solution']}\n"
+        f"   - Establish AI governance framework\n"
+        f"   - Train key personnel\n\n"
+        f"2. **Phase 2: Department Pilots** (2-3 months)\n"
+        f"   - Implement AI use cases in {top_departments[0]['Department']}\n"
+        f"   - Measure results\n\n"
+        f"3. **Phase 3: Expansion** (3-6 months)\n"
+        f"   - Roll out to additional departments\n"
+        f"   - Begin subscription consolidation\n"
+        f"   - Scale security controls\n\n"
+        f"\n[Learn more about Preamble solutions and pricing](https://preamble.com/pricing)"
+    )
+    return report, comparison
+
+
+CUSTOM_CSS = f"""
+/* General Styles */
+.gradio-container {{
+    max-width: 1200px !important;
+    margin: auto !important;
+    padding: 2rem !important;
+    background-color: {BRAND_COLORS['dark_bg']} !important;
+    font-family: 'Inter', sans-serif !important;
+    color: {BRAND_COLORS['light_text']} !important;
+    border-radius: 12px !important;
+    box-shadow: 0 8px 16px rgba(0, 0, 0, 0.15) !important;
+    font-size: 16px !important;
+}}
+/* Main container */
+.main-container {{
+    display: flex !important;
+    flex-direction: column !important;
+    gap: 2rem !important;
+}}
+/* Section styling */
+.section-container {{
+    background-color: {BRAND_COLORS['secondary']} !important;
+    border-radius: 12px !important;
+    padding: 2.5rem !important;
+    margin-bottom: 2rem !important;
+    box-shadow: 0 4px 8px rgba(0, 0, 0, 0.15) !important;
+    border: 1px solid {BRAND_COLORS['primary']} !important;
+    transition: all 0.3s ease !important;
+}}
+.section-title {{
+    font-size: 2.2rem !important;
+    color: {BRAND_COLORS['accent']} !important;
+    font-weight: 700 !important;
+    margin-bottom: 1.5rem !important;
+    line-height: 1.3 !important;
+    text-align: center !important;
+    text-shadow: 0px 1px 2px rgba(0,0,0,0.2) !important;
+}}
+.subsection-title {{
+    color: {BRAND_COLORS['dark_text']} !important;
+    font-size: 1.6rem !important;
+    font-weight: 600 !important;
+    margin: 1.5rem 0 1rem !important;
+    border-bottom: 2px solid {BRAND_COLORS['accent']} !important;
+    padding-bottom: 0.5rem !important;
+    display: inline-block !important;
+}}
+/* Input fields styling */
+.input-row {{
+    display: grid !important;
+    grid-template-columns: repeat(auto-fit, minmax(280px, 1fr)) !important;
+    gap: 1.5rem !important;
+    margin: 1.5rem 0 !important;
+}}
+.number-input, .text-input, .dropdown-input, .radio-input {{
+    background-color: {BRAND_COLORS['primary']} !important;
+    border: 2px solid {BRAND_COLORS['light_bg']} !important;
+    border-radius: 10px !important;
+    padding: 1rem !important;
+    color: {BRAND_COLORS['light_text']} !important;
+    box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1) !important;
+    transition: all 0.2s ease-in-out !important;
+    font-size: 16px !important;
+}}
+.number-input:focus, .text-input:focus, .dropdown-input:focus, .radio-input:focus {{
+    border-color: {BRAND_COLORS['accent']} !important;
+    box-shadow: 0 0 0 4px rgba(255, 199, 0, 0.3) !important;
+    outline: none !important;
+    transform: translateY(-2px) !important;
+}}
+/* Input Labels - Use the defined label_text color */
+.label-text, .textinput label, .dropdown-input label, .radio-input label, .number-input label {{        color: {BRAND_COLORS['accent']} !important;
     margin-bottom: 0.75rem !important;
-    font-size: 1.1rem !important;
-}
+    font-weight: 600 !important;
+    font-size: 17px !important;
+    display: block !important;
+    letter-spacing: 0.5px !important;
+}}
+/* Table/Dataframe styling */
+.table-container {{
+    background-color: {BRAND_COLORS['light_bg']} !important;
+    border-radius: 10px !important;
+    margin: 1.5rem 0 !important;
+    overflow: auto !important; /* Changed from 'hidden' to 'auto' to allow scrolling when needed */
+    border: 2px solid {BRAND_COLORS['primary']} !important;
+    box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1) !important;
+    max-height: 400px !important; /* Set maximum height */
+}}
+.table-container table {{
+    color: {BRAND_COLORS['input_text']} !important;
+    width: 100% !important;
+    border-collapse: collapse !important;
+    font-size: 16px !important;
+    display: table !important; /* Ensure proper table display */
+    table-layout: fixed !important; /* Fixed layout for better control */
+}}
+.table-container th {{
+    background-color: {BRAND_COLORS['table_header']} !important;
+    color: #FFFFFF !important;
+    padding: 1rem !important;
+    text-align: left !important;
+    border-bottom: 2px solid {BRAND_COLORS['primary']} !important;
+    font-size: 16px !important;
+    text-transform: uppercase !important;
+    letter-spacing: 0.5px !important;
+}}
+.table-container td {{
+    padding: 1rem !important;
+    text-align: left !important;
+    border-bottom: 1px solid {BRAND_COLORS['table_header']} !important;
+}}
+.table-container tr:nth-child(odd) {{
+    background-color: {BRAND_COLORS['table_row_odd']} !important;
+}}
+.table-container tr:nth-child(even) {{
+    background-color: {BRAND_COLORS['table_row_even']} !important;
+}}
+.table-container tr:hover {{
+    background-color: rgba(255, 199, 0, 0.1) !important;
+}}
 
-/* Calculate button */
-.calculate-button {
-    background-color: #FFC700 !important;
-    color: #081423 !important;
+/* Department table specific styling */
+.department-table {{
+    height: 350px !important;
+    min-height: 350px !important;
+    overflow-y: visible !important;
+}}
+.department-table table {{
+    height: auto !important;
+}}
+.department-table td {{
+    white-space: normal !important;
+    overflow: visible !important;
+    text-overflow: clip !important;
+    padding: 12px !important;
+}}
+/* Results container - General background and text */
+.results-container {{
+    background-color: {BRAND_COLORS['result_bg']} !important;
+    border-radius: 12px !important;
+    padding: 2.5rem !important;
+    margin-top: 2rem !important;
+    color: {BRAND_COLORS['result_text']} !important;
+    box-shadow: 0 4px 8px rgba(0, 0, 0, 0.15) !important;
+}}
+/* Result Card - Specific overrides */
+.result-card {{
+    background-color: {BRAND_COLORS['result_bg']} !important;
+    border-radius: 10px !important;
+    padding: 2rem !important;
+    margin: 1.5rem 0 !important;
+    box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1) !important;
+    font-size: 16px !important;
+    color: {BRAND_COLORS['result_text']} !important;
+    border-left: 5px solid {BRAND_COLORS['accent']} !important;
+}}
+/* Result Card Headings*/
+.result-card h3 {{
+    font-size: 1.8rem !important;
+    margin-bottom: 1.2rem !important;
+    color: {BRAND_COLORS['result_text']} !important;
+    font-weight: 600 !important;
+    border-bottom: 2px solid {BRAND_COLORS['accent']} !important;
+    padding-bottom: 0.5rem !important;
+}}
+.result-card h4 {{
+    font-size: 1.5rem !important;
+    margin: 1.2rem 0 0.8rem !important;
+    color: {BRAND_COLORS['result_text']} !important;
+    font-weight: 600 !important;
+}}
+/* Result card text */
+.result-card p, .result-card strong {{
+    color: {BRAND_COLORS['result_text']} !important;
+    font-size: 16px !important;
+    line-height: 1.7 !important;
+    margin-bottom: 1rem !important;
+}}
+.result-card ul {{
+    padding-left: 1.5rem !important;
+    margin-bottom: 1rem !important;
+}}
+.result-card li {{
+    margin-bottom: 0.5rem !important;
+}}
+/* Buttons styling */
+.calculate-button {{
+    background-color: {BRAND_COLORS['accent']} !important;
+    color: {BRAND_COLORS['button_text']} !important;
     font-weight: 600 !important;
     padding: 1rem 2rem !important;
-    border-radius: 8px !important;
+    border-radius: 50px !important;
+    margin-top: 2rem !important;
+    font-size: 1.2rem !important;
+    min-width: 200px !important;
+    transition: all 0.3s ease !important;
+    display: inline-block !important;
+    border: none !important;
+    cursor: pointer !important;
+    box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1) !important;
+    text-align: center !important;
+    letter-spacing: 0.5px !important;
+}}
+.calculate-button:hover {{
+    background-color: #e6b400 !important;
+    box-shadow: 0 6px 10px rgba(255, 199, 0, 0.3) !important;
+    transform: translateY(-2px) !important;
+}}
+.back-button {{
+    background-color: transparent !important;
+    color: {BRAND_COLORS['light_text']} !important;
+    border: 2px solid {BRAND_COLORS['light_text']} !important;
+    font-weight: 600 !important;
+    padding: 0.8rem 1.5rem !important;
+    border-radius: 50px !important;
     margin-top: 2rem !important;
     font-size: 1.1rem !important;
-    width: auto !important;
-    transition: all 0.2s !important;
-}
-
-.calculate-button:hover {
+    min-width: 150px !important;
+    transition: all 0.3s ease !important;
+    display: inline-block !important;
+    cursor: pointer !important;
+    text-align: center !important;
+}}
+.back-button:hover {{
+    background-color: rgba(255, 255, 255, 0.1) !important;
+    box-shadow: 0 2px 4px rgba(255, 255, 255, 0.1) !important;
+}}
+/* Button container */
+.button-container {{
+    display: flex !important;
+    justify-content: space-between !important;
+    align-items: center !important;
+    margin-top: 2rem !important;
+    gap: 1rem !important;
+}}
+.center-button-container {{
+    display: flex !important;
+    justify-content: center !important;
+    align-items: center !important;
+    margin-top: 2rem !important;
+    gap: 1rem !important;
+}}
+/* Chart container */
+.chart-container {{
+    background-color: {BRAND_COLORS['light_bg']} !important;
+    border-radius: 12px !important;
+    padding: 2rem !important;
+    margin: 1.5rem 0 !important;
+    height: 500px !important;
+    border: 2px solid {BRAND_COLORS['primary']} !important;
+    color: {BRAND_COLORS['input_text']} !important;
+    box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1) !important;
+}}
+/* Tab styling */
+.tab-nav {{
+    padding: 0 !important;
+    margin-bottom: 2rem !important;
+    display: flex !important;
+    justify-content: center !important;
+    gap: 0.5rem !important;
+    flex-wrap: wrap !important;
+}}
+.tab-nav button {{
+    color: {BRAND_COLORS['light_text']} !important;
+    padding: 0.8rem 1.8rem !important;
+    border-radius: 50px !important;
+    opacity: 0.9 !important;
+    border: 2px solid {BRAND_COLORS['primary']} !important;
+    background-color: {BRAND_COLORS['secondary']} !important;
+    cursor: pointer !important;
+    font-size: 16px !important;
+    font-weight: 600 !important;
+    transition: all 0.3s ease !important;
+    min-width: 180px !important;
+    text-align: center !important;
+}}
+.tab-nav button.selected {{
+    background-color: {BRAND_COLORS['primary']} !important;
+    color: {BRAND_COLORS['light_text']} !important;
+    opacity: 1 !important;
+    box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1) !important;
+}}
+.tab-nav button:hover {{
     transform: translateY(-2px) !important;
-    box-shadow: 0 4px 12px rgba(255, 199, 0, 0.3) !important;
-}
-"""
-
-def recommend_preamble_solution(deployment_type, existing_ai, monthly_budget):
-    """
-    Updated recommendation logic:
-    - If budget < $27,000/mo:
-      - For existing AI: Recommend Guardrails
-      - For new platform: Recommend SaaS
-    - If budget >= $27,000/mo or On-Premises: Recommend Enterprise
-    """
-    try:
-        budget_value = float(monthly_budget) if monthly_budget is not None else 0
-    except (ValueError, TypeError):
-        budget_value = 0
-
-    # Always recommend Enterprise for on-premises deployment
-    if deployment_type == "On-Premises":
-        return (
-            "Enterprise",
-            """### 🏢 Enterprise License
-            Perfect for organizations that need:
-            - On-premises deployment
-            - Complete compliance controls
-            - Dedicated support team
-            - Custom integrations
-            Starting at $27,000/month"""
-        )
-    
-    # For budgets under $27,000/month
-    if budget_value < 27000:
-        if existing_ai == "Yes, we have existing AI applications":
-            return (
-                "Guardrails",
-                """### ⚡ Guardrails
-                Perfect for integrating with existing AI:
-                - Quick API integration
-                - Usage-based pricing
-                - $0.005 per API call
-                - Flexible scaling options"""
-            )
-        else:
-            return (
-                "SaaS",
-                """### 🚀 SaaS Platform
-                Perfect for teams getting started with AI:
-                - Full platform features
-                - Quick cloud deployment
-                - Regular updates
-                - $50 per user/month"""
-            )
-    
-    # For budgets $27,000/month and above
-    return (
-        "Enterprise",
-        """### 🏢 Enterprise License
-        Perfect for organizations that need:
-        - Full enterprise features
-        - Complete compliance controls
-        - Dedicated support team
-        - Custom integrations
-        Starting at $27,000/month"""
-    )
+    box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1) !important;
+}}
+/* Timeline progress indicator */
+.timeline-container {{
+    display: flex !important;
+    justify-content: space-between !important;
+    align-items: center !important;
+    margin: 0 auto 2rem auto !important;
+    position: relative !important;
+    max-width: 800px !important;
+    padding: 15px 20px !important;
+    background-color: {BRAND_COLORS['secondary']} !important;
+    border-radius: 8px !important;
+}}
+.timeline-container:after {{
+    content: "" !important;
+    position: absolute !important;
+    height: 3px !important;
+    background-color: {BRAND_COLORS['primary']} !important;
+    top: 50% !important;
+    left: 0 !important;
+    right: 0 !important;
+    z-index: 1 !important;
+}}
+.timeline-step {{
+    padding: 8px 15px !important;
+    border-radius: 6px !important;
+    font-weight: 600 !important;
+    font-size: 14px !important;
+    color: {BRAND_COLORS['light_text']} !important;
+    background-color: {BRAND_COLORS['primary']} !important;
+    z-index: 2 !important;
+    position: relative !important;
+    transition: all 0.3s ease !important;
+    box-shadow: 0 2px 4px rgba(0, 0, 0, 0.2) !important;
+    cursor: default !important;
+}}
+.timeline-step.active {{
+    background-color: {BRAND_COLORS['accent']} !important;
+    color: {BRAND_COLORS['dark_text']} !important;
+    transform: translateY(-2px) !important;
+    box-shadow: 0 4px 8px rgba(255, 199, 0, 0.3) !important;
+}}
+@media (max-width: 768px) {{
+    .timeline-container {{
+        flex-direction: column !important;
+    }}
 
-def calculate_roi(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] = inputs
-
-    # Annual labor savings
-    total_hours_saved = num_employees * hours_saved_per_week * 52
-    labor_cost_savings = total_hours_saved * hourly_wage
-
-    # Compliance savings - ensure numeric types
-    compliance_savings = sum(
-        float(row["Expected Violations"]) * (float(row["Penalty"]) + float(row["Attorney Cost"]))
-        for _, row in compliance_data.iterrows()
-    )
-    
-    # Total benefits
-    total_benefits = labor_cost_savings + revenue_increase + compliance_savings
-
-    # In-house costs
-    total_costs_build = (
-        initial_platform_cost +
-        (num_ai_hires * avg_salary) +
-        ai_maintenance_cost +
-        ai_security_cost
-    )
+    .timeline-container {{
+        gap: 10px !important;
+    }}
 
-    # Preamble costs
-    solution_type, _ = recommend_preamble_solution(deployment_type, existing_ai, monthly_budget)
-    if solution_type == "Enterprise":
-        total_costs_preamble = 27000 * 12
-    elif solution_type == "SaaS":
-        total_costs_preamble = num_employees * 50 * 12
-    else:  # Guardrails
-        total_costs_preamble = estimated_api_calls * 0.005 * 12
+    .timeline-container:after {{
+        display: none !important;
+    }}
 
-    # 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
+    .timeline-step {{
+        width: 100% !important;
+        text-align: center !important;
+    }}
+}}
+/* Info card */
+.info-card {{
+    background-color: rgba(255, 199, 0, 0.1) !important;
+    border-left: 4px solid {BRAND_COLORS['accent']} !important;
+    padding: 1.2rem !important;
+    margin: 1.5rem 0 !important;
+    border-radius: 0 8px 8px 0 !important;
+}}
+.info-card-title {{
+    font-weight: 600 !important;
+    margin-bottom: 0.5rem !important;
+    color: {BRAND_COLORS['accent']} !important;
+}}
+.helper-text {{
+    color: {BRAND_COLORS['accent']} !important;
+    font-size: 14px !important;
+    margin-top: 0.5rem !important;
+    opacity: 0.9 !important;
+}}
+/* Responsive adjustments */
+@media (max-width: 768px) {{
+    .progress-container:after {{
+        left: 0 !important;
+        right: 0 !important;
+    }}
+    .section-title {{
+        font-size: 1.8rem !important;
+    }}
+    .subsection-title {{
+        font-size: 1.4rem !important;
+    }}
+    .calculate-button, .back-button {{
+        width: 100% !important;
+    }}
+}}
+/* Responsive adjustments for smaller screens */
+@media (max-width: 768px) {{
+    .input-row {{
+        grid-template-columns: 1fr !important; /* Stack inputs vertically */
+    }}
+}}
+"""
 
-    return roi_build, roi_preamble, total_benefits, total_costs_build, total_costs_preamble
 
 def create_app():
     with gr.Blocks(css=CUSTOM_CSS, theme=gr.themes.Default()) as roi_app:
+        current_step = gr.State(value=1)
+        total_employees_state = gr.State(value=90)
+
         with gr.Column(elem_classes="main-container"):
-            # Product Selection
-            with gr.Column(elem_classes="product-selector"):
-                gr.Markdown("# Find Your Ideal Preamble Solution", elem_classes="selector-title")
-                
-                gr.Markdown("Deployment Preference", elem_classes="section-title")
-                deployment_type = gr.Radio(
-                    ["Cloud/SaaS", "On-Premises"],
-                    label="",
-                    value="Cloud/SaaS",
-                    elem_classes="radio-group"
-                )
-
-                gr.Markdown("AI Implementation Status", elem_classes="section-title")
-                existing_ai = gr.Radio(
-                    ["Yes, we have existing AI applications", "No, we need a full platform"],
-                    label="",
-                    value="Yes, we have existing AI applications",
-                    elem_classes="radio-group"
-                )
+            gr.Markdown("# Preamble AI Security ROI Calculator",
+                        elem_classes="section-title")
+            with gr.Row(elem_classes="timeline-container"):
+                step1_indicator = gr.Markdown(
+                    "1. Organization Profile",
+                    elem_classes="timeline-step active",
+                    elem_id="step1-indicator")
+                step2_indicator = gr.Markdown(
+                    "2. Department Assessment",
+                    elem_classes="timeline-step",
+                    elem_id="step2-indicator")
+                step3_indicator = gr.Markdown(
+                    "3. Tools & Compliance",
+                    elem_classes="timeline-step",
+                    elem_id="step3-indicator")
+                step4_indicator = gr.Markdown(
+                    "4. Results",
+                    elem_classes="timeline-step",
+                    elem_id="step4-indicator")
+
+            with gr.Column(visible=True,
+                           elem_id="step1",
+                           elem_classes="section-container") as step1:
+                gr.Markdown("## Organization Profile",
+                            elem_classes="section-title")
+                gr.Markdown("""<div class="info-card">
+                <div class="info-card-title">Getting Started</div>
+                Let's understand your organization's needs better. Fill out the basic information below to help us provide tailored recommendations.
+                </div>""")
 
                 with gr.Row(elem_classes="input-row"):
-                    monthly_budget = gr.Number(
-                        label="Monthly Budget ($)",
-                        value=50000,
-                        elem_classes="number-input"
+                    industry = gr.Dropdown(label="Industry",
+                                           choices=list(
+                                               industry_ai_risks.keys()),
+                                           value="Financial Services",
+                                           elem_classes="dropdown-input")
+                    gr.Markdown(
+                        """<div class="helper-text">Your industry helps us identify specific AI security risks and compliance requirements.</div>"""
                     )
-                    estimated_api_calls = gr.Number(
-                        label="Estimated Monthly API Calls",
-                        value=100000,
-                        elem_classes="number-input",
-                        visible=False
+
+                with gr.Row(elem_classes="input-row"):
+                    org_size = gr.Number(label="Number of Employees",
+                                         value=90,
+                                         elem_classes="number-input",
+                                         precision=0)
+                    gr.Markdown(
+                        """<div class="helper-text">This helps us scale recommendations appropriately for your organization size.</div>"""
                     )
 
-                recommendation_box = gr.Markdown(elem_classes="recommendation-panel")
+                with gr.Row(elem_classes="input-row"):
+                    monthly_budget = gr.Number(
+                        label="Monthly Budget for AI Security ($)",
+                        value=30000,
+                        elem_classes="number-input",
+                        precision=0)
+                    gr.Markdown(
+                        """<div class="helper-text">We'll recommend solutions that fit within your budget constraints.</div>"""
+                    )
 
-            # Results
-            with gr.Row(elem_classes="results-container"):
-                with gr.Column():
-                    build_roi_box = gr.Markdown(
-                        "### Building In-House\n**ROI:** --%\n**Total Costs:** $0",
-                        elem_classes="result-card"
+                with gr.Row(elem_classes="input-row"):
+                    has_ai_app = gr.Radio(
+                        label=
+                        "Do you have an existing in-house AI application?",
+                        choices=[("Yes", True), ("No", False)],
+                        value=False,
+                        elem_classes="radio-input")
+                    gr.Markdown(
+                        """<div class="helper-text">This helps determine if we should focus on securing existing AI systems or implementing new ones.</div>"""
                     )
-                with gr.Column():
-                    preamble_roi_box = gr.Markdown(
-                        "### Using Preamble\n**ROI:** --%\n**Total Costs:** $0",
-                        elem_classes="result-card"
+
+                with gr.Row(elem_classes="input-row"):
+                    api_calls = gr.Number(label="Estimated Monthly API Calls",
+                                          value=10000,
+                                          elem_classes="number-input",
+                                          precision=0)
+                    gr.Markdown(
+                        """<div class="helper-text">For organizations with existing AI systems, this helps estimate the cost of our Guardrails solution (based on 8hr workday, 5 days/week).</div>"""
                     )
 
-            # Calculator
-            with gr.Column(elem_classes="calculator-tabs"):
+                with gr.Row(elem_classes="button-container"):
+                    next_button1 = gr.Button("Next: Department Assessment →",
+                                             elem_classes="calculate-button")
+
+
+            with gr.Column(visible=False,
+                           elem_id="step2",
+                           elem_classes="section-container") as step2:
+                gr.Markdown("## Department Assessment",
+                            elem_classes="section-title")
+                gr.Markdown("""<div class="info-card">
+                <div class="info-card-title">Department Breakdown</div>
+                Review and customize the department data below to match your organization's structure. This helps us identify which departments will benefit most from AI implementation.
+                </div>""")
+
+                gr.Markdown(
+                    "#### Edit the table below to match your organization",
+                    elem_classes="subsection-title")
+                department_data = gr.Dataframe(
+                    value=default_departments,
+                    headers=[
+                        "Department", "Number of Employees", "Average Salary",
+                        "Hours Per Week on Manual Tasks"
+                    ],
+                    datatype=["str", "number", "number", "number"],
+                    col_count=(4, "fixed"),
+                    elem_classes="table-container department-table", 
+                    interactive=True)
+
+                gr.Markdown("""<div class="helper-text">
+                ✓ Add or modify departments as needed<br>
+                ✓ Update employee counts to reflect your team structure<br>
+                ✓ Estimate time spent on tasks that could be automated with AI
+                </div>""")
+
+                with gr.Row(elem_classes="button-container"):
+                    back_button2 = gr.Button("← Back",
+                                             elem_classes="back-button")
+                    next_button2 = gr.Button("Next: Tools & Compliance →",
+                                             elem_classes="calculate-button")
+
+
+            with gr.Column(visible=False,
+                           elem_id="step3",
+                           elem_classes="section-container") as step3:
+                gr.Markdown("## Tools & Compliance",
+                            elem_classes="section-title")
+
+                gr.Markdown("""<div class="info-card">
+                <div class="info-card-title">Optimize Costs & Reduce Risk</div>
+                In this section, we'll identify potential savings from consolidating tools with AI capabilities and estimate compliance risk reduction based on your industry.
+                </div>""")
+
                 with gr.Tabs():
-                    with gr.Tab("Organization"):
-                        num_employees = gr.Slider(
-                            label="Number of Employees",
-                            minimum=1, maximum=5000, value=215,
-                            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 compensation"
+                    with gr.Tab("Technology & Subscriptions"):
+                        gr.Markdown("#### Current Software & Tools",
+                                    elem_classes="subsection-title")
+                        gr.Markdown(
+                            """Review and edit your current technology expenses that could be optimized with AI solutions."""
                         )
 
-                    with gr.Tab("Build Costs"):
-                        initial_platform_cost = gr.Number(
-                            label="Initial Development Cost ($)",
-                            value=1000000
+                        subscription_data = gr.Dataframe(
+                            value=default_subscriptions,
+                            headers=[
+                                "Tool Category",
+                                "Current Monthly Cost per User",
+                                "Number of Users", "Estimated Reduction %"
+                            ],
+                            datatype=["str", "number", "number", "number"],
+                            col_count=(4, "fixed"),
+                            elem_classes="table-container",
+                            interactive=True)
+
+                        gr.Markdown("""<div class="helper-text">
+                        Adjust the "Estimated Reduction %" to reflect how much you believe AI could help reduce costs in each category.
+                        </div>""")
+
+                    with gr.Tab("Compliance & Risk"):
+                        gr.Markdown("#### Regulatory Requirements",
+                                    elem_classes="subsection-title")
+                        gr.Markdown(
+                            """These compliance requirements are tailored to your industry. Edit to match your specific situation."""
                         )
-                        with gr.Row():
-                            num_ai_hires = gr.Number(
-                                label="Number of AI Personnel",
-                                value=1
-                            )
-                            avg_salary = gr.Number(
-                                label="Average Annual Salary ($)",
-                                value=200000
-                            )
-                        with gr.Row():
-                            ai_maintenance_cost = gr.Number(
-                                label="Annual Maintenance ($)",
-                                value=500000
-                            )
-                            ai_security_cost = gr.Number(
-                                label="Security & Compliance ($)",
-                                value=250000
-                            )
-
-                    with gr.Tab("Compliance"):
+
                         compliance_data = gr.Dataframe(
-                            value=default_compliance_df,
-                            headers=["Regulation", "Expected Violations", "Penalty", "Attorney Cost"],
-                            datatype="pandas"
+                            value=industry_compliance_data[
+                                "Financial Services"],
+                            headers=[
+                                "Regulation", "Potential Violations",
+                                "Penalty", "Attorney Cost"
+                            ],
+                            datatype=["str", "number", "number", "number"],
+                            col_count=(4, "fixed"),
+                            elem_classes="table-container",
+                            interactive=True)
+
+                        gr.Markdown("""<div class="helper-text">
+                        ✓ "Potential Violations" estimates how many compliance issues AI security could prevent<br>
+                        ✓ "Penalty" represents average regulatory fines per violation<br>
+                        ✓ "Attorney Cost" includes legal expenses for addressing violations
+                        </div>""")
+
+                with gr.Row(elem_classes="button-container"):
+                    back_button3 = gr.Button("← Back",
+                                             elem_classes="back-button")
+                    next_button3 = gr.Button("Next: Build vs Buy Comparison →",
+                                             elem_classes="calculate-button")
+                    calculate_button = gr.Button(
+                        "Generate Recommendations ✨",
+                        elem_classes="calculate-button")
+
+
+            with gr.Column(visible=False,
+                           elem_id="step3_5",
+                           elem_classes="section-container") as step3_5:
+                gr.Markdown("## Build vs Buy Analysis",
+                            elem_classes="section-title")
+
+                gr.Markdown("""<div class="info-card">
+                <div class="info-card-title">Compare Building In-House vs Preamble</div>
+                Adjust the values below to compare the cost of building and maintaining your own AI security platform versus using Preamble's enterprise solution.
+                </div>""")
+
+                with gr.Row(elem_classes="input-row"):
+                    initial_dev_cost = gr.Number(
+                        label="Initial Development Cost ($)",
+                        value=1000000,
+                        elem_classes="number-input",
+                        precision=0)
+
+                with gr.Row(elem_classes="input-row"):
+                    num_ai_personnel = gr.Number(
+                        label="Number of AI Personnel",
+                        value=1,
+                        elem_classes="number-input",
+                        precision=0)
+                    avg_annual_salary = gr.Number(
+                        label="Average Annual Salary ($)",
+                        value=200000,
+                        elem_classes="number-input",
+                        precision=0)
+
+                with gr.Row(elem_classes="input-row"):
+                    annual_maintenance = gr.Number(
+                        label="Annual Maintenance ($)",
+                        value=500000,
+                        elem_classes="number-input",
+                        precision=0)
+                    security_compliance = gr.Number(
+                        label="Security & Compliance ($)",
+                        value=250000,
+                        elem_classes="number-input",
+                        precision=0)
+
+                with gr.Row(elem_classes="button-container"):
+                    back_button3_5 = gr.Button("← Back",
+                                             elem_classes="back-button")
+                    calculate_button_with_comparison = gr.Button(
+                        "Generate Recommendations with Comparison ✨",
+                        elem_classes="calculate-button")
+
+
+            with gr.Column(visible=False,
+                           elem_id="step4",
+                           elem_classes="section-container") as step4:
+                gr.Markdown("## Your Personalized AI Security Plan",
+                            elem_classes="section-title")
+
+                gr.Markdown("""<div class="info-card">
+                <div class="info-card-title">Your Customized Recommendation</div>
+                Based on your inputs, we've generated a comprehensive AI security implementation plan tailored to your organization's needs, budget, and industry requirements.
+                </div>""")
+
+                with gr.Tabs():
+                    with gr.Tab("Executive Summary"):
+                        recommendation_output = gr.Markdown(
+                            elem_classes="result-card")
+
+                    with gr.Tab("Savings Analysis"):
+                        gr.Markdown("### Potential Cost Savings",
+                                    elem_classes="subsection-title")
+                        gr.Markdown(
+                            """This chart illustrates the potential monthly savings across your different tool categories by implementing AI solutions."""
                         )
+                        subscription_chart = gr.Plot(
+                            elem_classes="chart-container")
 
-                    with gr.Tab("Benefits"):
-                        revenue_increase = gr.Number(
-                            label="Estimated Annual Revenue Increase ($)",
-                            value=500000,
-                            info="Projected revenue growth"
+                    with gr.Tab("Build vs Buy Comparison"):
+                        gr.Markdown("### Build vs Buy Analysis",
+                                    elem_classes="subsection-title")
+                        gr.Markdown(
+                            """This chart compares the cost of building and maintaining your own AI security platform versus using Preamble's enterprise solution over 3 years."""
                         )
+                        build_vs_buy_chart = gr.Plot(
+                            elem_classes="chart-container")
 
-                calculate_button = gr.Button("Calculate ROI", elem_classes="calculate-button")
+                gr.Markdown(
+                    """<div class="helper-text" style="margin-top: 20px; text-align: center;">
+                Want to learn more about implementing these recommendations? Contact our team at <a href="mailto:sales@preamble.com" style="color: #FFC700;">sales@preamble.com</a>
+                </div>""")
 
-        def update_results(*inputs):
-            roi_build, roi_preamble, benefits, costs_build, costs_preamble = calculate_roi(inputs)
-            
-            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}"
-            )
-            
-            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
-
-        def update_api_calls_visibility(existing_ai, deployment_type, budget):
-            try:
-                budget_value = float(budget) if budget is not None else 0
-            except (ValueError, TypeError):
-                budget_value = 0
-                
-            should_show = (
-                deployment_type != "On-Premises" and
-                (existing_ai == "Yes, we have existing AI applications" or
-                 budget_value < 27000)
-            )
-            return gr.update(visible=should_show)
-
-        # Connect events
-        for input_elem in [deployment_type, existing_ai, monthly_budget]:
-            input_elem.change(
-                fn=lambda d, e, b: recommend_preamble_solution(d, e, b)[1],
-                inputs=[deployment_type, existing_ai, monthly_budget],
-                outputs=[recommendation_box]
+                with gr.Row(elem_classes="center-button-container"):
+                    restart_button = gr.Button("Start Over",
+                                               elem_classes="calculate-button")
+
+
+            def update_progress_indicator(step):
+                return [
+                    gr.update(elem_classes="timeline-step active" if i ==
+                              step else "timeline-step") for i in range(1, 5)
+                ]
+
+            def go_to_step1(step):
+                indicators = update_progress_indicator(1)
+                return (gr.update(visible=True), gr.update(visible=False),
+                        gr.update(visible=False), gr.update(visible=False),
+                        gr.update(visible=False), 1, *indicators)
+
+            def go_to_step2(org_size, step):
+                indicators = update_progress_indicator(2)
+                total_employees = int(org_size) if org_size is not None else 0
+                updated_departments = default_departments.copy()
+                if total_employees != updated_departments[
+                        'Number of Employees'].sum():
+                    updated_departments.loc[
+                        updated_departments.index[-1],
+                        'Number of Employees'] += total_employees - updated_departments[
+                            'Number of Employees'].sum()
+
+                updated_subscriptions = default_subscriptions.copy()
+                updated_subscriptions['Number of Users'] = total_employees
+
+                return (gr.update(visible=False), gr.update(visible=True),
+                        gr.update(visible=False), gr.update(visible=False), 
+                        gr.update(visible=False), gr.update(value=2),
+                        gr.update(value=total_employees), gr.update(value=updated_departments),
+                        gr.update(value=updated_subscriptions), *indicators)
+
+            def go_to_step3(industry, step):
+                indicators = update_progress_indicator(3)
+                new_compliance_data = industry_compliance_data.get(
+                    industry, industry_compliance_data["Other"])
+                new_compliance_data.columns = [
+                    "Regulation", "Potential Violations", "Penalty",
+                    "Attorney Cost"
+                ]
+                return (gr.update(visible=False), gr.update(visible=False),
+                        gr.update(visible=True), gr.update(visible=False),
+                        gr.update(visible=False), gr.update(value=new_compliance_data), 
+                        gr.update(value=3), *indicators)
+
+            def go_to_step3_5(step):
+                indicators = update_progress_indicator(3)
+                return (gr.update(visible=False), gr.update(visible=False),
+                        gr.update(visible=False), gr.update(visible=True),
+                        gr.update(visible=False), gr.update(value=3), *indicators)
+
+            def generate_recommendations_and_go_to_step4(
+                    industry, org_size, monthly_budget, has_ai_app, api_calls,
+                    department_df, subscription_df, compliance_df, step):
+                dept_roi_results = calculate_department_roi(department_df)
+                sub_savings = calculate_subscription_savings(subscription_df)
+                comp_savings = calculate_compliance_savings(compliance_df)
+                report = generate_recommendations(industry, org_size,
+                                                  monthly_budget, has_ai_app,
+                                                  dept_roi_results,
+                                                  sub_savings, comp_savings,
+                                                  api_calls)[0]  # Only get the report
+                chart = create_subscription_savings_chart(subscription_df)
+                indicators = update_progress_indicator(4)
+                return (gr.update(visible=False), gr.update(visible=False),
+                        gr.update(visible=False), gr.update(visible=False),
+                        gr.update(visible=True), report, chart, chart, *indicators)
+
+            def generate_recommendations_with_comparison(
+                    industry, org_size, monthly_budget, has_ai_app, api_calls,
+                    department_df, subscription_df, compliance_df, 
+                    initial_dev_cost, num_ai_personnel, avg_annual_salary,
+                    annual_maintenance, security_compliance, step):
+                dept_roi_results = calculate_department_roi(department_df)
+                sub_savings = calculate_subscription_savings(subscription_df)
+                comp_savings = calculate_compliance_savings(compliance_df)
+                report, comparison = generate_recommendations(
+                    industry, org_size, monthly_budget, has_ai_app,
+                    dept_roi_results, sub_savings, comp_savings, api_calls,
+                    initial_dev_cost, num_ai_personnel, avg_annual_salary,
+                    annual_maintenance, security_compliance)
+                subscription_chart = create_subscription_savings_chart(subscription_df)
+                comparison_chart = create_build_vs_buy_chart(comparison)
+                indicators = update_progress_indicator(4)
+                return (gr.update(visible=False), gr.update(visible=False),
+                        gr.update(visible=False), gr.update(visible=False),
+                        gr.update(visible=True), report, subscription_chart, 
+                        comparison_chart, *indicators)
+
+
+            next_button1.click(fn=go_to_step2,
+                               inputs=[org_size, current_step],
+                               outputs=[
+                                   step1, step2, step3, step3_5, step4, current_step,
+                                   total_employees_state, department_data,
+                                   subscription_data, step1_indicator,
+                                   step2_indicator, step3_indicator, step4_indicator
+                               ])
+
+            back_button2.click(fn=go_to_step1,
+                               inputs=[current_step],
+                               outputs=[
+                                   step1, step2, step3, step3_5, step4, current_step,
+                                   step1_indicator, step2_indicator, step3_indicator,
+                                   step4_indicator
+                               ])
+            next_button2.click(fn=go_to_step3,
+                               inputs=[industry, current_step],
+                               outputs=[
+                                   step1, step2, step3, step3_5, step4, compliance_data,
+                                   current_step, step1_indicator, step2_indicator,
+                                   step3_indicator, step4_indicator
+                               ])
+
+
+            back_button3.click(fn=go_to_step2,
+                               inputs=[org_size, current_step],
+                               outputs=[
+                                   step1, step2, step3, step3_5, step4, current_step,
+                                   total_employees_state, department_data,
+                                   subscription_data, step1_indicator,
+                                   step2_indicator, step3_indicator, step4_indicator
+                               ])
+
+            next_button3.click(fn=go_to_step3_5,
+                              inputs=[current_step],
+                              outputs=[
+                                  step1, step2, step3, step3_5, step4, 
+                                  current_step, step1_indicator, step2_indicator, 
+                                  step3_indicator, step4_indicator
+                              ])
+
+            back_button3_5.click(fn=go_to_step3,
+                               inputs=[industry, current_step],
+                               outputs=[
+                                   step1, step2, step3, step3_5, step4, compliance_data,
+                                   current_step, step1_indicator, step2_indicator,
+                                   step3_indicator, step4_indicator
+                               ])
+
+            calculate_button.click(fn=generate_recommendations_and_go_to_step4,
+                                   inputs=[
+                                       industry, org_size, monthly_budget,
+                                       has_ai_app, api_calls, department_data,
+                                       subscription_data, compliance_data,
+                                       current_step
+                                   ],
+                                   outputs=[
+                                       step1, step2, step3, step3_5, step4,
+                                       recommendation_output,
+                                       subscription_chart, build_vs_buy_chart, step1_indicator,
+                                       step2_indicator, step3_indicator, step4_indicator
+                                   ])
+
+            calculate_button_with_comparison.click(
+                fn=generate_recommendations_with_comparison,
+                inputs=[
+                    industry, org_size, monthly_budget, has_ai_app, 
+                    api_calls, department_data, subscription_data, 
+                    compliance_data, initial_dev_cost, num_ai_personnel, 
+                    avg_annual_salary, annual_maintenance, 
+                    security_compliance, current_step
+                ],
+                outputs=[
+                    step1, step2, step3, step3_5, step4,
+                    recommendation_output, subscription_chart, 
+                    build_vs_buy_chart, step1_indicator, step2_indicator, 
+                    step3_indicator, step4_indicator
+                ]
             )
-            
-            if input_elem in [deployment_type, existing_ai]:
-                input_elem.change(
-                    fn=update_api_calls_visibility,
-                    inputs=[existing_ai, deployment_type, monthly_budget],
-                    outputs=[estimated_api_calls]
-                )
-
-        # Connect 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]
-        )
+
+            restart_button.click(fn=go_to_step1,
+                                 inputs=[current_step],
+                                 outputs=[
+                                     step1, step2, step3, step3_5, step4, current_step,
+                                     step1_indicator, step2_indicator, step3_indicator,
+                                     step4_indicator
+                                 ])
 
         return roi_app
 
+
 app = create_app()
 
 if __name__ == "__main__":
     app.launch()
 
-
-
-
-                            
+#Preamble, Inc. 2025
+#AI ROI Calc
+#web https://preamble.com