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| import gradio as gr | |
| import pandas as pd | |
| import numpy as np | |
| import plotly.graph_objects as go | |
| BRAND_COLORS = { | |
| '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": [10, 15, 8, 12, 20, 25], | |
| "Average Salary": [95000, 85000, 70000, 75000, 85000, 65000], | |
| "Hours Per Week on Manual Tasks": [20, 20, 15, 10, 12, 18] | |
| }) | |
| default_subscriptions = pd.DataFrame({ | |
| "Tool Category": [ | |
| "CRM", "ERP", "Project Management", "Cloud Storage", | |
| "SaaS Subscriptions" | |
| ], | |
| "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] | |
| }) | |
| 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] | |
| }) | |
| } | |
| industry_ai_risks = { | |
| "Financial Services": [ | |
| "Data privacy breaches with sensitive financial information", | |
| "Sharing Material nonpublic information (MNPI) to unauthorized 3rd parties", | |
| "Compliance violations with regulatory frameworks (e.g., GDPR, CCPA, SOX, PCI DSS, GLBA)" | |
| ], | |
| "Healthcare": [ | |
| "Patient data confidentiality breaches", "HIPAA compliance violations", | |
| "Data corruption","Sensitive healthcare information sharing to unauthorized 3rd parties", | |
| "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","Insider threats accessing data through AI systems", | |
| "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" | |
| ] | |
| } | |
| 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"] | |
| } | |
| # --- Calculation and Recommendation Functions --- | |
| def create_subscription_savings_chart(subscription_data): | |
| # Use pandas vectorized operations instead of loop | |
| df = subscription_data.copy() | |
| # Calculate costs and savings in one go | |
| df['monthly_cost'] = df["Current Monthly Cost per User"] * df["Number of Users"] | |
| df['savings'] = df['monthly_cost'] * (df["Estimated Reduction %"] / 100) | |
| # Convert to lists for plotly | |
| categories = df["Tool Category"].tolist() | |
| current_costs = df['monthly_cost'].tolist() | |
| savings = df['savings'].tolist() | |
| # Create the figure with all styling applied at once | |
| fig = go.Figure() | |
| # Add both traces at once | |
| 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)) | |
| # Set layout all at once | |
| 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'])), | |
| 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 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) | |
| 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 | |
| } | |
| def calculate_compliance_savings(compliance_data): | |
| if compliance_data.empty: | |
| return {"total_savings": 0, "key_regulations": []} | |
| 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) | |
| 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} | |
| 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)" | |
| if monthly_budget < 27000: | |
| if has_ai_app: | |
| solution = "Guardrails Only" | |
| monthly_cost = api_calls * 0.005 | |
| description = f""" | |
| #### ⚡ [Guardrails Only - Compliance](https://preamble.com/pricing) | |
| Perfect for integrating with your existing AI application: | |
| - Quick API integration. | |
| - Pre-built security and privacy guardrails. | |
| - Basic compliance templates. | |
| - Agentless operation. | |
| - Free Trial available. | |
| - Free Shadow AI Tool {shadow_ai_blocker} | |
| $0.005 per API call (Est. monthly cost: ${monthly_cost:,.2f} at {api_calls:,} calls/month) | |
| [Learn more about Preamble pricing options](https://preamble.com/pricing)""" | |
| image = "🛡️" | |
| else: | |
| solution = "SMB" | |
| description = f""" | |
| #### 🚀 [SMB](https://preamble.com/pricing) | |
| 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. | |
| - Free Shadow AI Tool {shadow_ai_blocker} | |
| $50 per user/month | |
| [Learn more about Preamble pricing options](https://preamble.com/pricing)""" | |
| image = "🏢" | |
| elif org_size >= 1000 or industry in high_risk_industries: | |
| solution = "Enterprise" | |
| description = f""" | |
| #### 🏢 [Enterprise License](https://preamble.com/pricing) | |
| Perfect for large or high-risk organizations: | |
| - Complete compliance controls. | |
| - Custom AI Assistant Development Support. | |
| - Dedicated support team. | |
| - Private AI Marketplace. | |
| - Custom integrations. | |
| - Private cloud/on-premise options. | |
| - Free Shadow AI Tool {shadow_ai_blocker} | |
| Starting at $27,000/month with discounts for multi-year deals | |
| [Learn more about Preamble pricing options](https://preamble.com/pricing)""" | |
| image = "🚀" | |
| elif org_size >= 100: | |
| solution = "SMB" | |
| description = f""" | |
| #### 🚀 [SMB](https://preamble.com/pricing) | |
| Perfect for teams getting started with AI: | |
| - Full platform features. | |
| - Standard compliance templates. | |
| - Custom AI Assistants. | |
| - Safe AI Search Agent | |
| - Secure Knowledge Base. | |
| - SaaS Hosting. | |
| - Free Shadow AI Tool {shadow_ai_blocker} | |
| $50 per user/month | |
| [Learn more about Preamble pricing options](https://preamble.com/pricing)""" | |
| image = "🏢" | |
| else: | |
| solution = "Guardrails Only" | |
| monthly_cost = api_calls * 0.005 | |
| description = f""" | |
| #### ⚡ [Guardrails Only - Compliance](https://preamble.com/pricing) | |
| Perfect for integrating with your existing AI application: | |
| - Quick API integration. | |
| - Pre-built security and privacy guardrails. | |
| - Basic compliance templates. | |
| - Agentless operation. | |
| - Free Trial available. | |
| - Free Shadow AI Tool {shadow_ai_blocker} | |
| $0.005 per API call (Est. monthly cost: ${monthly_cost:,.2f} at {api_calls:,} calls/month) | |
| [Learn more about Preamble pricing options](https://preamble.com/pricing)""" | |
| image = "🛡️" | |
| return {"solution": solution, "description": description, "image": image} | |
| def calculate_build_vs_buy_comparison(initial_dev_cost=1000000, num_ai_personnel=1, avg_annual_salary=200000, annual_maintenance=500000, security_compliance=250000): | |
| # Convert inputs to numbers if they aren't already | |
| try: | |
| initial_dev_cost = float(initial_dev_cost) | |
| num_ai_personnel = int(num_ai_personnel) | |
| avg_annual_salary = float(avg_annual_salary) | |
| annual_maintenance = float(annual_maintenance) | |
| security_compliance = float(security_compliance) | |
| except (ValueError, TypeError): | |
| # Default values if conversion fails | |
| initial_dev_cost = 1000000.0 | |
| num_ai_personnel = 1 | |
| avg_annual_salary = 200000.0 | |
| annual_maintenance = 500000.0 | |
| security_compliance = 250000.0 | |
| 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 if three_year_build_cost > 0 else 0 | |
| 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"]) | |
| comparison = calculate_build_vs_buy_comparison( | |
| initial_dev_cost, | |
| num_ai_personnel, | |
| avg_annual_salary, | |
| annual_maintenance, | |
| security_compliance | |
| ) | |
| dept_recommendations = "" | |
| for i, dept in enumerate(top_departments): | |
| dept_recommendations += f"\n{i+1}. **{dept['Department']}** (ROI: {dept['ROI']:.1f}%)\n" | |
| dept_recommendations += f" - Annual cost savings: ${dept['Annual Cost Savings']:,.2f}\n" | |
| dept_recommendations += f" - Hours saved annually: {dept['Annual Hours Saved']:,.0f}\n" | |
| dept_recommendations += " - Top AI use cases:\n" | |
| for use_case in dept["Use Cases"]: | |
| dept_recommendations += f" - {use_case}\n" | |
| security_text = "\n\n### Key Security Considerations\n" | |
| for risk in industry_risks: | |
| security_text += f"- {risk}\n" | |
| subscription_text = "\n### Subscription & Tool Optimization\n" | |
| subscription_text += f"- **Current annual spend:** ${subscription_savings['total_current_cost']:,.2f}\n" | |
| subscription_text += f"- **Potential annual savings:** ${subscription_savings['total_savings']:,.2f} ({subscription_savings['savings_percentage']:.1f}%)\n" | |
| subscription_text += "- **Recommendation:** Evaluate consolidation of overlapping tools with AI capabilities\n" | |
| compliance_text = "\n### Compliance Risk Reduction\n" | |
| compliance_text += f"- **Potential risk exposure reduction:** ${compliance_savings['total_savings']:,.2f}\n" | |
| compliance_text += "- **Key regulations to address:**\n" | |
| for reg in compliance_savings['key_regulations']: | |
| compliance_text += f" - {reg['name']}: ${reg['cost']:,.2f} risk exposure\n" | |
| build_vs_buy_text = "\n### Build vs Buy Analysis\n" | |
| build_vs_buy_text += "- **Building in-house:**\n" | |
| build_vs_buy_text += f" - First year cost: ${comparison['first_year_build']:,.2f}\n" | |
| build_vs_buy_text += f" - Ongoing annual cost: ${comparison['ongoing_annual_build']:,.2f}\n" | |
| build_vs_buy_text += f" - 3-year total cost: ${comparison['three_year_build']:,.2f}\n\n" | |
| build_vs_buy_text += "- **Preamble Enterprise:**\n" | |
| build_vs_buy_text += f" - Annual cost: ${comparison['annual_preamble']:,.2f}\n" | |
| build_vs_buy_text += f" - 3-year total cost: ${comparison['three_year_preamble']:,.2f}\n\n" | |
| build_vs_buy_text += f"- **3-year savings with Preamble:** ${comparison['three_year_savings']:,.2f} ({comparison['savings_percentage']:.1f}%)\n" | |
| build_vs_buy_text += f"- **ROI:** {((comparison['three_year_savings'] / comparison['three_year_preamble']) * 100):,.1f}%\n" | |
| # Improve formatting for Executive Summary with line breaks between items | |
| executive_summary = "## Executive Summary\n" | |
| executive_summary += f"- **Industry:** {industry}\n" | |
| executive_summary += f"- **Organization Size:** {org_size:,} employees\n" | |
| executive_summary += f"- **Monthly Budget:** ${monthly_budget:,.2f}\n" | |
| executive_summary += f"- **Existing AI Application:** {'Yes' if has_ai_app else 'No'}\n" | |
| executive_summary += f"- **Total Potential Annual Savings:** ${total_dept_savings + subscription_savings['total_savings'] + compliance_savings['total_savings']:,.2f}\n\n" | |
| # Format implementation roadmap with proper line breaks | |
| implementation_roadmap = "## Implementation Roadmap\n\n" | |
| implementation_roadmap += "1. **Phase 1: Security Foundation** (1-2 months)\n" | |
| implementation_roadmap += f" - Deploy Preamble {solution_rec['solution']}\n" | |
| implementation_roadmap += " - Establish AI governance framework\n" | |
| implementation_roadmap += " - Train key personnel\n\n" | |
| implementation_roadmap += "2. **Phase 2: Department Pilots** (2-3 months)\n" | |
| implementation_roadmap += f" - Implement AI use cases in {top_departments[0]['Department']}\n" | |
| implementation_roadmap += " - Measure results\n\n" | |
| implementation_roadmap += "3. **Phase 3: Expansion** (3-6 months)\n" | |
| implementation_roadmap += " - Roll out to additional departments\n" | |
| implementation_roadmap += " - Begin subscription consolidation\n" | |
| implementation_roadmap += " - Scale security controls\n\n" | |
| report = ( | |
| f"# AI Implementation & Security Recommendation\n{executive_summary}" | |
| f"## Recommended Preamble Solution\n{solution_rec['image']} {solution_rec['description']}\n\n" | |
| f"{build_vs_buy_text}\n" | |
| f"## Department Recommendations\nThe following departments show the highest potential ROI for AI implementation:{dept_recommendations}\n" | |
| f"{subscription_text}{compliance_text}{security_text}\n\n{implementation_roadmap}" | |
| 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-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; | |
| }} | |
| /* 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; | |
| }} | |
| /* Enhanced Markdown Rendering - All heading levels */ | |
| .result-card h1 {{ | |
| font-size: 2.2rem !important; | |
| margin: 1.5rem 0 1.2rem !important; | |
| color: {BRAND_COLORS['accent']} !important; | |
| font-weight: 700 !important; | |
| border-bottom: 2px solid {BRAND_COLORS['accent']} !important; | |
| padding-bottom: 0.6rem !important; | |
| }} | |
| .result-card h2 {{ | |
| font-size: 2rem !important; | |
| margin: 1.4rem 0 1rem !important; | |
| color: {BRAND_COLORS['result_text']} !important; | |
| font-weight: 700 !important; | |
| border-bottom: 1px solid {BRAND_COLORS['accent']} !important; | |
| padding-bottom: 0.5rem !important; | |
| }} | |
| .result-card h3 {{ | |
| font-size: 1.8rem !important; | |
| margin: 1.3rem 0 1rem !important; | |
| color: {BRAND_COLORS['result_text']} !important; | |
| font-weight: 600 !important; | |
| border-bottom: 1px solid rgba(255, 199, 0, 0.5) !important; | |
| padding-bottom: 0.4rem !important; | |
| display: inline-block !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 h5 {{ | |
| font-size: 1.25rem !important; | |
| margin: 1rem 0 0.6rem !important; | |
| color: {BRAND_COLORS['result_text']} !important; | |
| font-weight: 600 !important; | |
| }} | |
| /* Result card text elements */ | |
| .result-card p {{ | |
| color: {BRAND_COLORS['result_text']} !important; | |
| font-size: 16px !important; | |
| line-height: 1.7 !important; | |
| margin-bottom: 1rem !important; | |
| }} | |
| .result-card strong, .result-card b {{ | |
| color: {BRAND_COLORS['accent']} !important; | |
| font-weight: 600 !important; | |
| }} | |
| .result-card em, .result-card i {{ | |
| font-style: italic !important; | |
| }} | |
| .result-card a {{ | |
| color: {BRAND_COLORS['accent']} !important; | |
| text-decoration: underline !important; | |
| transition: all 0.2s ease !important; | |
| }} | |
| .result-card a:hover {{ | |
| opacity: 0.8 !important; | |
| }} | |
| /* Lists styling */ | |
| .result-card ul, .result-card ol {{ | |
| padding-left: 1.8rem !important; | |
| margin: 0.8rem 0 1.2rem 0.5rem !important; | |
| }} | |
| .result-card li {{ | |
| margin-bottom: 0.7rem !important; | |
| color: {BRAND_COLORS['result_text']} !important; | |
| }} | |
| /* Code blocks */ | |
| .result-card code {{ | |
| background-color: rgba(0, 0, 0, 0.3) !important; | |
| padding: 0.2rem 0.4rem !important; | |
| border-radius: 4px !important; | |
| font-family: monospace !important; | |
| font-size: 0.9rem !important; | |
| }} | |
| .result-card pre {{ | |
| background-color: rgba(0, 0, 0, 0.3) !important; | |
| padding: 1rem !important; | |
| border-radius: 6px !important; | |
| overflow-x: auto !important; | |
| margin: 1rem 0 !important; | |
| }} | |
| /* Blockquotes */ | |
| .result-card blockquote {{ | |
| border-left: 4px solid {BRAND_COLORS['accent']} !important; | |
| padding-left: 1rem !important; | |
| margin-left: 0 !important; | |
| margin-right: 0 !important; | |
| font-style: italic !important; | |
| color: rgba(255, 255, 255, 0.9) !important; | |
| }} | |
| /* Tables */ | |
| .result-card table {{ | |
| width: 100% !important; | |
| border-collapse: collapse !important; | |
| margin: 1.5rem 0 !important; | |
| }} | |
| .result-card th {{ | |
| background-color: {BRAND_COLORS['primary']} !important; | |
| color: {BRAND_COLORS['light_text']} !important; | |
| padding: 0.8rem !important; | |
| text-align: left !important; | |
| font-weight: 600 !important; | |
| }} | |
| .result-card td {{ | |
| border: 1px solid rgba(255, 255, 255, 0.2) !important; | |
| padding: 0.8rem !important; | |
| }} | |
| .result-card tr:nth-child(even) {{ | |
| background-color: rgba(0, 0, 0, 0.2) !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: 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; | |
| 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 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; | |
| }} | |
| .timeline-container {{ | |
| gap: 10px !important; | |
| }} | |
| .timeline-container:after {{ | |
| display: none !important; | |
| }} | |
| .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 */ | |
| }} | |
| }} | |
| """ | |
| 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"): | |
| gr.Markdown("# Preamble AI Security ROI Calculator", | |
| elem_classes="section-title") | |
| with gr.Row(elem_classes="timeline-container"): | |
| step1_indicator = gr.Markdown( | |
| "Organization Profile", | |
| elem_classes="timeline-step active", | |
| elem_id="step1-indicator") | |
| step2_indicator = gr.Markdown( | |
| "Department Assessment", | |
| elem_classes="timeline-step", | |
| elem_id="step2-indicator") | |
| step3_indicator = gr.Markdown( | |
| "Tools & Compliance", | |
| elem_classes="timeline-step", | |
| elem_id="step3-indicator") | |
| step4_indicator = gr.Markdown( | |
| "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"): | |
| 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>""" | |
| ) | |
| with gr.Row(elem_classes="input-row"): | |
| org_size = gr.Number(label="Number of Employees", | |
| value=90, | |
| minimum=10, | |
| elem_classes="number-input", | |
| precision=0) | |
| gr.Markdown( | |
| """<div class="helper-text">This helps us scale recommendations appropriately for your organization size (minimum 10 employees).</div>""" | |
| ) | |
| 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>""" | |
| ) | |
| 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.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>""" | |
| ) | |
| 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("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.""" | |
| ) | |
| 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.""" | |
| ) | |
| compliance_data = gr.Dataframe( | |
| 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>""") | |
| # Simplified output without tabs and plots for better performance | |
| recommendation_output = gr.Markdown( | |
| elem_classes="result-card", | |
| render=True) | |
| 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>""") | |
| 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) | |
| # Ensure org_size is valid | |
| try: | |
| total_employees = int(org_size) if org_size is not None else 0 | |
| if total_employees < 0: | |
| total_employees = 0 | |
| except (ValueError, TypeError): | |
| total_employees = 0 | |
| # Create copies of dataframes only once | |
| updated_departments = default_departments.copy() | |
| updated_subscriptions = default_subscriptions.copy() | |
| # Update department employee count efficiently | |
| if total_employees > 0: | |
| current_total = updated_departments['Number of Employees'].sum() | |
| if total_employees != current_total: | |
| # Distribute employees proportionally across departments | |
| if current_total > 0: | |
| ratio = total_employees / current_total | |
| for idx in updated_departments.index: | |
| new_count = round(updated_departments.at[idx, 'Number of Employees'] * ratio) | |
| updated_departments.at[idx, 'Number of Employees'] = max(1, new_count) # Ensure at least 1 employee per department | |
| else: | |
| # If current total is 0, distribute evenly | |
| dept_count = len(updated_departments) | |
| base_count = total_employees // dept_count | |
| remainder = total_employees % dept_count | |
| for idx in updated_departments.index: | |
| if idx < remainder: | |
| updated_departments.at[idx, 'Number of Employees'] = base_count + 1 | |
| else: | |
| updated_departments.at[idx, 'Number of Employees'] = base_count | |
| # Adjust to ensure exact total (due to rounding) | |
| current_sum = updated_departments['Number of Employees'].sum() | |
| if current_sum != total_employees: | |
| diff = total_employees - current_sum | |
| # Add or subtract the difference from the largest department to minimize impact | |
| largest_dept_idx = updated_departments['Number of Employees'].idxmax() | |
| updated_departments.at[largest_dept_idx, 'Number of Employees'] += diff | |
| # Ensure no negative values after adjustment | |
| if updated_departments.at[largest_dept_idx, 'Number of Employees'] < 1: | |
| # If the largest department would go negative, redistribute | |
| updated_departments.at[largest_dept_idx, 'Number of Employees'] = 1 | |
| remaining_diff = diff + 1 - updated_departments.at[largest_dept_idx, 'Number of Employees'] | |
| # Distribute remaining difference across other departments | |
| for idx in updated_departments.index: | |
| if idx != largest_dept_idx and remaining_diff != 0: | |
| if remaining_diff < 0 and updated_departments.at[idx, 'Number of Employees'] > 1: | |
| updated_departments.at[idx, 'Number of Employees'] -= 1 | |
| remaining_diff += 1 | |
| elif remaining_diff > 0: | |
| updated_departments.at[idx, 'Number of Employees'] += 1 | |
| remaining_diff -= 1 | |
| if remaining_diff == 0: | |
| break | |
| # Update all subscription users at once | |
| updated_subscriptions['Number of Users'] = total_employees | |
| # Return updates all at once | |
| 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 | |
| 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, *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): | |
| try: | |
| # Convert inputs to appropriate types with error handling | |
| try: | |
| org_size_val = int(org_size) if org_size is not None else 90 | |
| except (ValueError, TypeError): | |
| org_size_val = 90 | |
| try: | |
| monthly_budget_val = float(monthly_budget) if monthly_budget is not None else 30000 | |
| except (ValueError, TypeError): | |
| monthly_budget_val = 30000.0 | |
| try: | |
| api_calls_val = int(api_calls) if api_calls is not None else 10000 | |
| except (ValueError, TypeError): | |
| api_calls_val = 10000 | |
| try: | |
| initial_dev_cost_val = float(initial_dev_cost) if initial_dev_cost is not None else 1000000 | |
| except (ValueError, TypeError): | |
| initial_dev_cost_val = 1000000.0 | |
| try: | |
| num_ai_personnel_val = int(num_ai_personnel) if num_ai_personnel is not None else 1 | |
| except (ValueError, TypeError): | |
| num_ai_personnel_val = 1 | |
| try: | |
| avg_annual_salary_val = float(avg_annual_salary) if avg_annual_salary is not None else 200000 | |
| except (ValueError, TypeError): | |
| avg_annual_salary_val = 200000.0 | |
| try: | |
| annual_maintenance_val = float(annual_maintenance) if annual_maintenance is not None else 500000 | |
| except (ValueError, TypeError): | |
| annual_maintenance_val = 500000.0 | |
| try: | |
| security_compliance_val = float(security_compliance) if security_compliance is not None else 250000 | |
| except (ValueError, TypeError): | |
| security_compliance_val = 250000.0 | |
| # Validate dataframes and convert to default if invalid | |
| if department_df is None or department_df.empty: | |
| department_df = default_departments | |
| if subscription_df is None or subscription_df.empty: | |
| subscription_df = default_subscriptions | |
| if compliance_df is None or compliance_df.empty: | |
| compliance_df = industry_compliance_data.get(industry, industry_compliance_data["Other"]) | |
| # Calculate ROIs and savings | |
| 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_val, monthly_budget_val, has_ai_app, | |
| dept_roi_results, sub_savings, comp_savings, api_calls_val, | |
| initial_dev_cost_val, num_ai_personnel_val, avg_annual_salary_val, | |
| annual_maintenance_val, security_compliance_val) | |
| 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, *indicators) | |
| except Exception as e: | |
| # Provide a graceful error message instead of crashing | |
| error_message = f"An error occurred while generating recommendations: {str(e)}" | |
| 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), error_message, *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, 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, step1_indicator, step2_indicator, | |
| step3_indicator, step4_indicator | |
| ] | |
| ) | |
| 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 |