""" ARF 3.3.9 - Hugging Face Spaces Demo Using REAL OSS ARF Implementation Psychology-Optimized, Investor-Ready Interface """ import gradio as gr import time import random import json from datetime import datetime, timedelta import pandas as pd import numpy as np from typing import Dict, List, Optional # Import REAL ARF OSS implementation try: from arf import RiskEngine, PolicyEngine, ActionValidator from arf.license import LicenseManager from arf.scoring import BayesianRiskScorer ARF_AVAILABLE = True print("✅ Using REAL ARF OSS implementation") except ImportError: ARF_AVAILABLE = False print("⚠️ ARF OSS not installed, using simulation mode") # Fallback to simulation classes from utils.arf_simulation import RiskEngine, PolicyEngine, ActionValidator, LicenseManager, BayesianRiskScorer # Import local utilities from utils.psychology_layer import PsychologyEngine from utils.business_logic import BusinessValueCalculator from demo_scenarios import DEMO_SCENARIOS, get_scenario_context # Initialize engines risk_engine = RiskEngine() policy_engine = PolicyEngine() action_validator = ActionValidator() license_manager = LicenseManager() risk_scorer = BayesianRiskScorer() psych = PsychologyEngine() business = BusinessValueCalculator() # Track demo state class DemoState: def __init__(self): self.stats = { "actions_tested": 0, "risks_prevented": 0, "time_saved_minutes": 0, "trial_requests": 0, "high_risk_actions": 0, "start_time": time.time(), "license_upgrades": 0 } self.action_history = [] self.user_sessions = {} self.current_license = None def update_stat(self, stat_name: str, value: int = 1): if stat_name in self.stats: self.stats[stat_name] += value def get_stats(self) -> Dict: elapsed_hours = (time.time() - self.stats["start_time"]) / 3600 return { **self.stats, "actions_per_hour": round(self.stats["actions_tested"] / max(elapsed_hours, 0.1), 1), "reliability_score": min(99.9, 95 + (self.stats["risks_prevented"] / max(self.stats["actions_tested"], 1)) * 5), "session_count": len(self.user_sessions), "avg_risk_score": self._calculate_avg_risk() } def _calculate_avg_risk(self) -> float: if not self.action_history: return 0.0 return sum(h.get("risk_score", 0) for h in self.action_history) / len(self.action_history) demo_state = DemoState() # CSS for psychological persuasion PERSUASIVE_CSS = """ :root { --oss-blue: #1E88E5; --trial-gold: #FFB300; --pro-orange: #FF9800; --enterprise-dark: #FF6F00; --success-green: #4CAF50; --warning-orange: #FF9800; --danger-red: #F44336; --hf-orange: #FF6B00; } /* Hugging Face themed elements */ .hf-badge { background: linear-gradient(135deg, var(--hf-orange) 0%, #FF8B00 100%); color: white; padding: 6px 12px; border-radius: 15px; font-size: 11px; font-weight: bold; display: inline-flex; align-items: center; gap: 5px; margin: 2px; box-shadow: 0 2px 4px rgba(255, 107, 0, 0.2); } .hf-badge::before { content: "🤗"; font-size: 12px; } .hf-gradient { background: linear-gradient(135deg, var(--hf-orange) 0%, #FF8B00 100%); color: white; } /* Authority & Trust Signals */ .cert-badge { background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); color: white; padding: 8px 16px; border-radius: 20px; font-size: 12px; font-weight: bold; display: inline-block; margin: 2px; box-shadow: 0 2px 4px rgba(0,0,0,0.1); } /* Loss Aversion Highlighting */ .loss-aversion { border-left: 4px solid #F44336; padding-left: 12px; background: linear-gradient(to right, #FFF8E1, white); margin: 10px 0; border-radius: 0 8px 8px 0; } /* Social Proof Cards */ .social-proof { background: white; border-radius: 10px; padding: 15px; box-shadow: 0 4px 12px rgba(0,0,0,0.08); border: 1px solid #E0E0E0; transition: transform 0.2s; } .social-proof:hover { transform: translateY(-2px); box-shadow: 0 6px 16px rgba(0,0,0,0.12); } /* Scarcity Timer */ .scarcity-timer { background: linear-gradient(135deg, #FF6F00, #FFB300); color: white; padding: 10px 20px; border-radius: 10px; text-align: center; font-weight: bold; animation: pulse 2s infinite; } @keyframes pulse { 0% { box-shadow: 0 0 0 0 rgba(255, 111, 0, 0.4); } 70% { box-shadow: 0 0 0 10px rgba(255, 111, 0, 0); } 100% { box-shadow: 0 0 0 0 rgba(255, 111, 0, 0); } } /* Tier-specific theming */ .oss-theme { border-top: 4px solid var(--oss-blue); background: linear-gradient(to bottom, #E3F2FD, white); } .trial-theme { border-top: 4px solid var(--trial-gold); background: linear-gradient(to bottom, #FFF8E1, white); } .pro-theme { border-top: 4px solid var(--pro-orange); background: linear-gradient(to bottom, #FFF3E0, white); } .enterprise-theme { border-top: 4px solid var(--enterprise-dark); background: linear-gradient(to bottom, #FBE9E7, white); } /* Mechanical Gate Visualization */ .gate-visualization { display: flex; justify-content: space-between; margin: 20px 0; } .gate { width: 60px; height: 60px; border-radius: 50%; display: flex; align-items: center; justify-content: center; font-weight: bold; color: white; position: relative; } .gate.passed { background: var(--success-green); } .gate.failed { background: var(--danger-red); } .gate.pending { background: #BDBDBD; } .gate-line { height: 4px; flex-grow: 1; background: #E0E0E0; margin-top: 28px; } /* ROI Calculator */ .roi-calculator { background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); color: white; padding: 20px; border-radius: 15px; margin: 20px 0; } /* Action History Table */ .action-history { max-height: 300px; overflow-y: auto; margin: 10px 0; } .action-history table { width: 100%; border-collapse: collapse; } .action-history th { background: #f5f5f5; position: sticky; top: 0; padding: 8px; text-align: left; font-size: 12px; color: #666; } .action-history td { padding: 8px; border-bottom: 1px solid #eee; font-size: 12px; } /* Mobile Responsiveness */ @media (max-width: 768px) { .gate-visualization { flex-direction: column; align-items: center; } .gate-line { width: 4px; height: 30px; margin: 5px 0; } .social-proof { padding: 10px; } } /* Loading animation */ .loading-spinner { display: inline-block; width: 20px; height: 20px; border: 3px solid #f3f3f3; border-top: 3px solid var(--hf-orange); border-radius: 50%; animation: spin 1s linear infinite; } @keyframes spin { 0% { transform: rotate(0deg); } 100% { transform: rotate(360deg); } } """ def generate_trial_license(): """Generate a trial license key using ARF's license format""" import uuid license_id = str(uuid.uuid4())[:8].upper() return f"ARF-TRIAL-{license_id}-HF" def get_tier_color(tier): """Get color for license tier""" colors = { "oss": "#1E88E5", "trial": "#FFB300", "starter": "#FF9800", "professional": "#FF6F00", "enterprise": "#D84315" } return colors.get(tier, "#1E88E5") def format_risk_score(score): """Format risk score realistically using ARF's scoring""" if score is None: return "0.0%" # Ensure score is between 0.0 and 1.0 score = max(0.0, min(1.0, float(score))) # Apply realistic variance for demo purposes variance = random.uniform(-0.05, 0.05) final_score = score + variance # Ensure it's never exactly 0% or 100% final_score = max(0.05, min(0.95, final_score)) return f"{final_score*100:.1f}%" def assess_action_with_arf(action: str, context: Dict, license_key: Optional[str] = None): """Assess action using REAL ARF OSS implementation""" try: # Parse action using ARF's action parser parsed_action = action_validator.parse_action(action) # Create context dict for ARF arf_context = { "action": parsed_action, "environment": context.get("environment", "production"), "user_role": context.get("user_role", "developer"), "timestamp": datetime.now().isoformat(), "source": "huggingface_demo" } # Assess risk using ARF's Bayesian scorer risk_assessment = risk_scorer.assess( action=parsed_action, context=arf_context ) # Get risk score (convert to 0-1 range) risk_score = risk_assessment.get("risk_score", 0.5) # Get risk factors risk_factors = risk_assessment.get("risk_factors", []) # Validate against policies policy_result = policy_engine.evaluate( action=parsed_action, risk_score=risk_score, context=arf_context ) # Check license if provided license_info = {"tier": "oss", "features": []} if license_key: try: license_info = license_manager.validate(license_key) except: license_info = {"tier": "invalid", "features": []} # Determine recommendation if risk_score > 0.7: recommendation = "🚨 HIGH RISK: Immediate review required" demo_state.update_stat("high_risk_actions") elif risk_score > 0.4: recommendation = "⚠️ MODERATE RISK: Review recommended" else: recommendation = "✅ LOW RISK: Action appears safe" # Check if mechanical gates would apply has_mechanical_gates = license_info.get("tier") in ["trial", "starter", "professional", "enterprise"] # Simulate gate evaluation based on license tier if has_mechanical_gates: gate_results = simulate_gate_evaluation(risk_score, license_info.get("tier", "oss")) else: gate_results = { "gates_passed": 0, "total_gates": 3, "decision": "ADVISORY_ONLY", "details": "Mechanical gates require license" } return { "risk_score": risk_score, "risk_factors": risk_factors[:3], # Limit to top 3 "confidence": risk_assessment.get("confidence", 0.8), "recommendation": recommendation, "policy_result": policy_result, "license_tier": license_info.get("tier", "oss"), "license_name": license_info.get("name", "OSS Edition"), "gate_results": gate_results, "arf_version": "3.3.9", "assessment_id": str(uuid.uuid4())[:8] } except Exception as e: print(f"ARF assessment error: {e}") # Fallback to simulation return simulate_assessment(action, context, license_key) def simulate_assessment(action: str, context: Dict, license_key: Optional[str] = None): """Simulate assessment if ARF is not available""" # Simple risk scoring based on action content action_lower = action.lower() # Base risk score if "drop" in action_lower and "database" in action_lower: risk_score = 0.85 risk_factors = ["Destructive operation", "Irreversible data loss", "Production environment"] elif "delete" in action_lower: risk_score = 0.65 risk_factors = ["Data deletion", "Potential data loss", "Write operation"] elif "update" in action_lower and "where" not in action_lower: risk_score = 0.75 risk_factors = ["Mass update", "No WHERE clause", "Affects multiple records"] elif "grant" in action_lower and "admin" in action_lower: risk_score = 0.55 risk_factors = ["Privilege escalation", "Security implications", "Admin rights"] else: risk_score = 0.25 + random.random() * 0.3 risk_factors = ["Standard operation", "Low risk pattern"] # Context adjustments context_str = str(context).lower() if "production" in context_str: risk_score *= 1.3 risk_factors.append("Production environment") if "junior" in context_str or "intern" in context_str: risk_score *= 1.2 risk_factors.append("Junior operator") # Cap risk score risk_score = min(0.95, risk_score) # Determine license tier license_tier = "oss" license_name = "OSS Edition" if license_key: if "TRIAL" in license_key.upper(): license_tier = "trial" license_name = "Trial Edition" elif "PRO" in license_key.upper(): license_tier = "professional" license_name = "Professional Edition" elif "ENTERPRISE" in license_key.upper(): license_tier = "enterprise" license_name = "Enterprise Edition" # Generate recommendation if risk_score > 0.7: recommendation = "🚨 HIGH RISK: Immediate review required" elif risk_score > 0.4: recommendation = "⚠️ MODERATE RISK: Review recommended" else: recommendation = "✅ LOW RISK: Action appears safe" # Simulate gate evaluation gate_results = simulate_gate_evaluation(risk_score, license_tier) return { "risk_score": risk_score, "risk_factors": risk_factors, "confidence": 0.8 + random.random() * 0.15, "recommendation": recommendation, "policy_result": "evaluated", "license_tier": license_tier, "license_name": license_name, "gate_results": gate_results, "arf_version": "3.3.9 (simulated)", "assessment_id": str(uuid.uuid4())[:8] } def simulate_gate_evaluation(risk_score: float, license_tier: str): """Simulate mechanical gate evaluation""" gates_passed = 0 total_gates = 3 # Gate 1: Risk threshold if risk_score < 0.8: gates_passed += 1 # Gate 2: License check if license_tier != "oss": gates_passed += 1 # Gate 3: Resource availability (simulated) if random.random() > 0.3: # 70% chance gates_passed += 1 # Determine decision if license_tier == "oss": decision = "ADVISORY_ONLY" elif gates_passed >= 2: decision = "AUTONOMOUS" elif gates_passed >= 1: decision = "HUMAN_APPROVAL" else: decision = "BLOCKED" return { "gates_passed": gates_passed, "total_gates": total_gates, "decision": decision, "details": f"{gates_passed}/{total_gates} gates passed" } def create_demo_interface(): """Create the main demo interface""" with gr.Blocks( title="ARF 3.3.9 - Agentic Reliability Framework", theme=gr.themes.Soft( primary_hue="blue", secondary_hue="orange", neutral_hue="gray" ), css=PERSUASIVE_CSS ) as demo: # ===== HEADER: Psychological Value Proposition ===== gr.Markdown(f""" # 🤖 ARF 3.3.9 - Agentic Reliability Framework ### **From Advisory Warnings to Mechanical Enforcement**
Using {"REAL ARF OSS Implementation" if ARF_AVAILABLE else "Simulated ARF"} • Join 1,000+ developers using ARF for AI safety
""") # ===== STATISTICS BAR: Social Proof ===== with gr.Row(): with gr.Column(scale=1): stats_oss = gr.HTML(""" """) with gr.Column(scale=1): stats_time = gr.HTML(""" """) with gr.Column(scale=1): stats_roi = gr.HTML(""" """) with gr.Column(scale=1): stats_users = gr.HTML(""" """) # ===== CONTROL PANEL ===== with gr.Row(): with gr.Column(scale=2): # Scenario Selection scenario = gr.Dropdown( label="🚀 Select Scenario", choices=list(DEMO_SCENARIOS.keys()), value="DROP DATABASE production", interactive=True ) # Action Context (auto-filled based on scenario) context = gr.Textbox( label="📋 Context (auto-filled)", value="", interactive=False ) # License Key Input license_key = gr.Textbox( label="🔑 License Key (Optional)", placeholder="Enter ARF-TRIAL-XXX for 14-day free trial", value="" ) with gr.Row(): test_btn = gr.Button("🚦 Test Action", variant="primary", scale=2) trial_btn = gr.Button("🎁 Get 14-Day Trial", variant="secondary", scale=1) with gr.Column(scale=1): # License Info Display license_display = gr.HTML("""
⚠️ Advisory Mode Only
Risk assessment without enforcement
| Time | Action | Risk | License | Result |
|---|---|---|---|---|
| No actions yet | ||||
{'⚠️ 14-Day Trial
Full mechanical enforcement' if result['license_tier'] == 'trial' else '✅ Mechanical Enforcement Active
All gates operational' if result['license_tier'] != 'oss' else '⚠️ Advisory Mode Only
Risk assessment without enforcement'}
| Time | Action | Risk | License | Result |
|---|
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