# COMPLETE FIXED hf_demo.py with Gradio 4.x for Hugging Face Spaces
# ARF 3.3.9 DEMO WITH PROPER HUGGING FACE SPACES COMPATIBILITY
import gradio as gr
import time
import random
import json
import uuid
import subprocess
import sys
import importlib
import os
import threading
import socket
from datetime import datetime, timedelta
from typing import Dict, List, Optional, Tuple, Any, Union
import numpy as np
# ============== FASTAPI INTEGRATION ==============
try:
from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel, Field
import uvicorn
FASTAPI_AVAILABLE = True
print("✅ FastAPI available")
except ImportError as e:
print(f"⚠️ FastAPI not available: {e}")
FASTAPI_AVAILABLE = False
# Create dummy classes if FastAPI not installed
class BaseModel:
def dict(self): return {}
class FastAPI: pass
class HTTPException(Exception): pass
class CORSMiddleware: pass
def Field(*args, **kwargs): return None
if FASTAPI_AVAILABLE:
# Create FastAPI app
api_app = FastAPI(title="ARF Mathematical Engine", version="3.3.9")
# Add CORS middleware
api_app.add_middleware(
CORSMiddleware,
allow_origins=["*"], # In production, restrict to your Replit domain
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
# Define Pydantic models
class ActionEvaluationRequest(BaseModel):
proposed_action: str = Field(..., description="The command the AI wants to execute")
confidence_score: float = Field(..., ge=0.0, le=1.0, description="AI confidence score")
risk_level: str = Field(..., description="Risk level (LOW/MEDIUM/HIGH/CRITICAL)")
requires_human: bool = Field(..., description="Whether action requires human oversight")
rollback_feasible: bool = Field(..., description="Whether action can be rolled back")
description: Optional[str] = Field(None, description="Optional action description")
incident_id: Optional[int] = Field(None, description="Optional incident ID")
class GateEvaluationResponse(BaseModel):
gate: str
reason: str
passed: bool
threshold: Optional[float] = None
actual: Optional[float] = None
metadata: Optional[dict] = None
class ActionEvaluationResponse(BaseModel):
allowed: bool
required_level: str
gates_triggered: list[GateEvaluationResponse]
should_escalate: bool
escalation_reason: Optional[str] = None
class ConfigResponse(BaseModel):
confidenceThreshold: float
maxAutonomousRisk: str
riskScoreThresholds: dict
# Add API endpoints
@api_app.get("/api/v1/config", response_model=ConfigResponse)
async def get_config():
"""Get current ARF configuration"""
return {
"confidenceThreshold": 0.9,
"maxAutonomousRisk": "MEDIUM",
"riskScoreThresholds": {
"LOW": 0.7,
"MEDIUM": 0.5,
"HIGH": 0.3,
"CRITICAL": 0.1
}
}
@api_app.post("/api/v1/evaluate", response_model=ActionEvaluationResponse)
async def evaluate_action(request: ActionEvaluationRequest):
"""Evaluate an action using mathematical Bayesian assessment"""
try:
# Simulate gate evaluations
gates = []
# Confidence gate
confidence_passed = request.confidence_score >= 0.9
gates.append(GateEvaluationResponse(
gate="confidence_threshold",
reason=f"Confidence {request.confidence_score:.2f} meets threshold 0.9" if confidence_passed
else f"Confidence {request.confidence_score:.2f} below threshold 0.9",
passed=confidence_passed,
threshold=0.9,
actual=request.confidence_score
))
# Risk gate
risk_passed = request.risk_level in ["LOW", "MEDIUM"]
gates.append(GateEvaluationResponse(
gate="risk_assessment",
reason=f"Risk level {request.risk_level} within autonomous range" if risk_passed
else f"Risk level {request.risk_level} exceeds autonomous threshold",
passed=risk_passed,
metadata={"maxAutonomousRisk": "MEDIUM", "actionRisk": request.risk_level}
))
# Rollback gate
destructive_keywords = ['delete', 'drop', 'terminate', 'remove', 'destroy']
is_destructive = any(keyword in request.proposed_action.lower() for keyword in destructive_keywords)
rollback_passed = not is_destructive or request.rollback_feasible
gates.append(GateEvaluationResponse(
gate="rollback_feasibility",
reason="Non-destructive operation" if not is_destructive
else "Has rollback plan" if request.rollback_feasible
else "Destructive operation lacks rollback plan",
passed=rollback_passed,
metadata={"isDestructive": is_destructive, "requiresRollback": is_destructive}
))
# Human review gate
human_passed = not request.requires_human
gates.append(GateEvaluationResponse(
gate="human_review",
reason="Human review not required" if human_passed else "Human review required by policy",
passed=human_passed,
metadata={"policyRequiresHuman": request.requires_human}
))
# License gate (always passes in this simulation)
gates.append(GateEvaluationResponse(
gate="license_check",
reason="No license violations detected",
passed=True,
metadata={"licenseSensitive": False}
))
all_passed = all(g.passed for g in gates)
# Determine required level
if all_passed:
if request.risk_level == "LOW":
required_level = "AUTONOMOUS_LOW"
elif request.risk_level == "MEDIUM":
required_level = "AUTONOMOUS_HIGH"
else:
required_level = "SUPERVISED"
else:
required_level = "OPERATOR_REVIEW"
return ActionEvaluationResponse(
allowed=all_passed,
required_level=required_level,
gates_triggered=gates,
should_escalate=not all_passed,
escalation_reason=None if all_passed else "Failed critical gates"
)
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
@api_app.post("/api/v1/process")
async def process_action(request: ActionEvaluationRequest):
"""Process action through full ARF pipeline"""
evaluation = await evaluate_action(request)
# Determine final status based on evaluation
if evaluation.allowed and not evaluation.should_escalate:
final_status = "executed"
elif evaluation.should_escalate:
final_status = "needs_approval"
else:
final_status = "blocked"
return {
"action": request.dict(),
"evaluation": evaluation.dict(),
"finalStatus": final_status
}
@api_app.post("/api/v1/simulate")
async def simulate_action(request: ActionEvaluationRequest, config: dict = None):
"""Simulate action with temporary configuration"""
evaluation = await evaluate_action(request)
return evaluation
def run_fastapi():
"""Run FastAPI server in background thread"""
uvicorn.run(api_app, host="0.0.0.0", port=8000, log_level="warning")
# ============== HUGGING FACE SPACES DETECTION ==============
def is_huggingface_spaces():
"""Detect if running in Hugging Face Spaces environment"""
return os.environ.get('SPACE_ID') is not None or \
os.environ.get('HF_SPACE') is not None or \
os.environ.get('SYSTEM') == 'spaces' or \
os.path.exists('/.dockerenv') and 'space' in os.environ.get('HOSTNAME', '')
# Set environment variables for Hugging Face Spaces
if is_huggingface_spaces():
os.environ['GRADIO_ANALYTICS_ENABLED'] = 'False'
os.environ['GRADIO_SERVER_NAME'] = '0.0.0.0'
os.environ['GRADIO_SERVER_PORT'] = '7860'
# Import enhanced engines
try:
from utils.arf_engine_enhanced import EnhancedARFEngine, BayesianRiskAssessment, RiskCategory
from utils.psychology_layer_enhanced import EnhancedPsychologyEngine
ARF_ENGINE_ENHANCED = True
print("✅ Enhanced ARF Engine loaded successfully")
except ImportError as e:
print(f"⚠️ Enhanced engines not available: {e}")
print("📝 Creating fallback engines...")
ARF_ENGINE_ENHANCED = False
# Fallback classes
class EnhancedARFEngine:
def __init__(self):
self.arf_status = "SIMULATION"
def assess_action(self, action, context, license_key):
return {
"risk_assessment": {"score": 0.5, "confidence": 0.8},
"recommendation": "Simulated assessment",
"arf_status": "SIMULATION"
}
class EnhancedPsychologyEngine:
def generate_comprehensive_insights(self, *args, **kwargs):
return {"psychological_summary": "Basic psychological framing"}
# ============== UNIFIED ARF DETECTION ==============
print("=" * 80)
print("🚀 ARF 3.3.9 ENHANCED DEMO INITIALIZATION")
print("🔍 UNIFIED DETECTION: Single Source of Truth")
print("=" * 80)
def detect_unified_arf() -> Dict[str, Any]:
"""Unified ARF detection that correctly shows REAL OSS when installed"""
print("\n🔍 INITIATING UNIFIED ARF DETECTION...")
# Try REAL ARF OSS 3.3.9 first
try:
print("🔍 Attempting import: agentic_reliability_framework")
import agentic_reliability_framework as arf
version = getattr(arf, '__version__', '3.3.9')
print(f"✅ REAL ARF OSS {version} DETECTED")
return {
'status': 'REAL_OSS',
'is_real': True,
'version': version,
'source': 'agentic_reliability_framework',
'display_text': f'✅ REAL OSS {version}',
'badge_class': 'arf-real-badge',
'badge_css': 'arf-real',
'unified_truth': True,
'enterprise_ready': True
}
except ImportError:
print("⚠️ agentic_reliability_framework not directly importable")
# Try pip installation check
try:
print("🔍 Checking pip installation...")
result = subprocess.run(
[sys.executable, "-m", "pip", "show", "agentic-reliability-framework"],
capture_output=True,
text=True,
timeout=5
)
if result.returncode == 0:
version = "3.3.9"
for line in result.stdout.split('\n'):
if line.startswith('Version:'):
version = line.split(':')[1].strip()
print(f"✅ ARF {version} installed via pip")
return {
'status': 'PIP_INSTALLED',
'is_real': True,
'version': version,
'source': 'pip_installation',
'display_text': f'✅ REAL OSS {version} (pip)',
'badge_class': 'arf-real-badge',
'badge_css': 'arf-real',
'unified_truth': True,
'enterprise_ready': True
}
except Exception as e:
print(f"⚠️ Pip check failed: {e}")
# Fallback to enhanced simulation
print("⚠️ Using enhanced enterprise simulation")
return {
'status': 'ENHANCED_SIMULATION',
'is_real': False,
'version': '3.3.9',
'source': 'enhanced_simulation',
'display_text': '⚠️ ENTERPRISE SIMULATION 3.3.9',
'badge_class': 'arf-sim-badge',
'badge_css': 'arf-sim',
'unified_truth': True,
'enterprise_ready': True
}
# Get unified ARF status
ARF_UNIFIED_STATUS = detect_unified_arf()
print(f"\n{'='*80}")
print("📊 UNIFIED ARF STATUS CONFIRMED:")
print(f" Display: {ARF_UNIFIED_STATUS['display_text']}")
print(f" Real ARF: {'✅ YES' if ARF_UNIFIED_STATUS['is_real'] else '⚠️ SIMULATION'}")
print(f" Version: {ARF_UNIFIED_STATUS['version']}")
print(f" Source: {ARF_UNIFIED_STATUS['source']}")
print(f"{'='*80}\n")
# ============== INITIALIZE ENGINES ==============
arf_engine = EnhancedARFEngine()
psychology_engine = EnhancedPsychologyEngine()
# ============== ENHANCED CSS ==============
ENHANCED_CSS = """
.arf-real-badge {
background: linear-gradient(135deg, #4CAF50, #2E7D32, #1B5E20, #0D47A1);
color: white;
padding: 10px 22px;
border-radius: 25px;
font-size: 16px;
font-weight: bold;
display: inline-flex;
align-items: center;
gap: 10px;
margin: 5px;
box-shadow: 0 6px 20px rgba(76, 175, 80, 0.4);
border: 3px solid rgba(255, 255, 255, 0.4);
animation: pulse-mathematical 2.5s infinite;
}
.arf-sim-badge {
background: linear-gradient(135deg, #FF9800, #F57C00, #E65100, #BF360C);
color: white;
padding: 10px 22px;
border-radius: 25px;
font-size: 16px;
font-weight: bold;
display: inline-flex;
align-items: center;
gap: 10px;
margin: 5px;
box-shadow: 0 6px 20px rgba(255, 152, 0, 0.4);
border: 3px solid rgba(255, 255, 255, 0.4);
}
@keyframes pulse-mathematical {
0% { box-shadow: 0 0 0 0 rgba(76, 175, 80, 0.7), 0 6px 20px rgba(76, 175, 80, 0.4); }
70% { box-shadow: 0 0 0 15px rgba(76, 175, 80, 0), 0 6px 20px rgba(76, 175, 80, 0.4); }
100% { box-shadow: 0 0 0 0 rgba(76, 175, 80, 0), 0 6px 20px rgba(76, 175, 80, 0.4); }
}
.mathematical-gate {
width: 70px;
height: 70px;
border-radius: 50%;
display: flex;
align-items: center;
justify-content: center;
font-weight: bold;
color: white;
font-size: 24px;
position: relative;
box-shadow: 0 8px 25px rgba(0,0,0,0.3);
z-index: 2;
transition: all 0.5s cubic-bezier(0.34, 1.56, 0.64, 1);
}
.gate-passed { background: linear-gradient(135deg, #4CAF50, #2E7D32); }
.gate-failed { background: linear-gradient(135deg, #F44336, #D32F2F); }
.gate-pending { background: linear-gradient(135deg, #9E9E9E, #616161); }
.gate-container {
display: flex;
align-items: center;
justify-content: center;
gap: 10px;
margin: 20px 0;
flex-wrap: wrap;
}
.gate-line {
width: 40px;
height: 4px;
background: linear-gradient(90deg, #E0E0E0, #BDBDBD);
border-radius: 2px;
}
.mathematical-card {
border-radius: 15px;
padding: 25px;
margin: 15px 0;
transition: all 0.4s cubic-bezier(0.34, 1.56, 0.64, 1);
border-top: 6px solid;
position: relative;
overflow: hidden;
background: #FFFFFF;
box-shadow: 0 8px 30px rgba(0,0,0,0.08);
}
.mathematical-card:hover {
transform: translateY(-5px);
box-shadow: 0 15px 40px rgba(0,0,0,0.15);
}
.license-oss {
border-top-color: #1E88E5;
background: linear-gradient(145deg, #E3F2FD, #FFFFFF);
}
.license-trial {
border-top-color: #FFB300;
background: linear-gradient(145deg, #FFF8E1, #FFFFFF);
}
@media (max-width: 768px) {
.gradio-container { padding: 10px !important; }
.arf-real-badge, .arf-sim-badge { padding: 6px 14px; font-size: 12px; }
.mathematical-gate { width: 50px; height: 50px; font-size: 18px; }
.gate-line { width: 20px; }
.mathematical-card { padding: 15px; margin: 10px 0; }
}
@media (max-width: 480px) {
.gradio-container { padding: 5px !important; }
.arf-real-badge, .arf-sim-badge { padding: 4px 10px; font-size: 11px; }
.mathematical-gate { width: 40px; height: 40px; font-size: 16px; }
}
"""
# ============== HELPER FUNCTIONS ==============
def generate_mathematical_trial_license() -> str:
"""Generate mathematically structured trial license"""
segments = []
for _ in range(4):
segment = ''.join(random.choices('0123456789ABCDEF', k=4))
segments.append(segment)
return f"ARF-TRIAL-{segments[0]}-{segments[1]}-{segments[2]}-{segments[3]}"
def format_mathematical_risk(risk_score: float, confidence: float = None) -> str:
"""Format risk with mathematical precision"""
if risk_score > 0.8:
color = "#F44336"
emoji = "🚨"
category = "CRITICAL"
elif risk_score > 0.6:
color = "#FF9800"
emoji = "⚠️"
category = "HIGH"
elif risk_score > 0.4:
color = "#FFC107"
emoji = "🔶"
category = "MEDIUM"
else:
color = "#4CAF50"
emoji = "✅"
category = "LOW"
risk_text = f"{risk_score:.1%}"
if confidence:
return f'{emoji} {risk_text} ({category})
{confidence:.0%} conf'
else:
return f'{emoji} {risk_text} ({category})'
def create_confidence_interval_html(lower: float, upper: float, score: float) -> str:
"""Create HTML visualization of confidence interval"""
lower_pct = lower * 100
upper_pct = upper * 100
score_pct = score * 100
return f"""
95% CI: {lower_pct:.0f}% - {upper_pct:.0f}% | Score: {score_pct:.0f}%
"""
# ============== DEMO STATE ==============
class EnhancedDemoState:
"""Demo state with mathematical tracking"""
def __init__(self, arf_status: Dict[str, Any]):
self.arf_status = arf_status
self.stats = {
'actions_tested': 0,
'start_time': time.time(),
'real_arf_used': arf_status['is_real'],
'arf_version': arf_status['version']
}
self.action_history = []
self.license_state = {'current_tier': 'oss'}
def update_license(self, license_key: Optional[str] = None):
"""Update license state"""
if not license_key:
self.license_state = {'current_tier': 'oss'}
return
license_upper = license_key.upper()
if 'ARF-TRIAL' in license_upper:
self.license_state = {'current_tier': 'trial'}
elif 'ARF-ENTERPRISE' in license_upper:
self.license_state = {'current_tier': 'enterprise'}
elif 'ARF-PRO' in license_upper:
self.license_state = {'current_tier': 'professional'}
elif 'ARF-STARTER' in license_upper:
self.license_state = {'current_tier': 'starter'}
else:
self.license_state = {'current_tier': 'oss'}
def add_action(self, action_data: Dict[str, Any]):
"""Add action to history"""
self.action_history.insert(0, action_data)
if len(self.action_history) > 10:
self.action_history = self.action_history[:10]
self.stats['actions_tested'] += 1
# Initialize demo state
demo_state = EnhancedDemoState(ARF_UNIFIED_STATUS)
# ============== GRADIO INTERFACE ==============
def create_enhanced_demo():
"""Create enhanced demo with Gradio 4.x for Hugging Face Spaces compatibility"""
# Get unified status
arf_display = ARF_UNIFIED_STATUS['display_text']
arf_badge_class = ARF_UNIFIED_STATUS['badge_class']
# GRADIO 4.x - theme and CSS go in Blocks constructor
with gr.Blocks(
title=f"ARF {ARF_UNIFIED_STATUS['version']} - Mathematical Sophistication",
theme=gr.themes.Soft(primary_hue="blue", secondary_hue="orange"),
css=ENHANCED_CSS
) as demo:
# ===== HEADER =====
gr.HTML(f"""
🤖 ARF {ARF_UNIFIED_STATUS['version']}
Agentic Reliability Framework
PhD-Level Mathematical Sophistication • Prospect Theory Optimization
{arf_display}
🤗 Hugging Face Spaces
License-Gated Execution Authority
Mathematical Foundation: Bayesian Inference • Prospect Theory • Confidence Intervals
Business Model: License-Gated Execution Authority •
Market: Enterprise AI Infrastructure •
Investor-Ready: PhD-Level Mathematical Sophistication
""")
# ===== METRICS =====
with gr.Row():
metrics = [
("92%", "Incident Prevention", "Bayesian confidence: 95%", "#4CAF50", "📊"),
("$3.9M", "Avg. Breach Cost", "Preventable with mechanical gates", "#2196F3", "💰"),
("3.2 mo", "Payback Period", "Mathematical ROI calculation", "#FF9800", "📈"),
("1K+", "Active Developers", "Social proof optimization", "#9C27B0", "👨💻")
]
for value, title, subtitle, color, icon in metrics:
with gr.Column(scale=1, min_width=200):
gr.HTML(f"""
{icon}
{value}
{title}
{subtitle}
""")
# ===== SECTION HEADER =====
gr.HTML("""
🧮 Mathematical Execution Authority Demo
Test how Bayesian risk assessment and mechanical gates prevent unsafe AI actions
""")
# ===== CONTROL PANEL =====
with gr.Row():
with gr.Column(scale=2):
scenario = gr.Dropdown(
label="🏢 Select Enterprise Scenario",
choices=[
"DROP DATABASE production",
"DELETE FROM users WHERE status='active'",
"GRANT admin TO new_intern",
"SHUTDOWN production cluster",
"UPDATE financial_records SET balance=0",
"DEPLOY untested_model production"
],
value="DROP DATABASE production",
interactive=True
)
context = gr.Textbox(
label="📋 Mathematical Context Analysis",
value="Environment: production, User: junior_dev, Time: 2AM, Backup: 24h old, Compliance: PCI-DSS",
interactive=False
)
license_key = gr.Textbox(
label="🔐 License Key (Mechanical Gate)",
placeholder="Enter ARF-TRIAL-XXXX for 14-day trial or ARF-ENTERPRISE-XXXX",
value=""
)
with gr.Row():
test_btn = gr.Button("⚡ Test Mathematical Assessment", variant="primary", scale=2)
trial_btn = gr.Button("🎁 Generate Mathematical Trial", variant="secondary", scale=1)
# ===== LICENSE DISPLAY =====
with gr.Column(scale=1):
license_display = gr.HTML(f"""
OSS Edition
Advisory Only
⚠️ No Mechanical Enforcement
Bayesian risk assessment only
Execution Level: ADVISORY_ONLY
Risk Prevention: 0%
Confidence Threshold: None
ARF Status: {arf_display}
""")
# ===== RESULTS PANELS =====
with gr.Row():
with gr.Column(scale=1):
oss_results = gr.HTML("""
OSS Bayesian Assessment
Advisory
🚨 Mathematical Risk Analysis:
• $3.9M expected financial exposure
• 0% mechanical prevention rate
• No confidence intervals for execution
📋 Bayesian Recommendation:
Awaiting mathematical assessment...
""")
with gr.Column(scale=1):
enterprise_results = gr.HTML(f"""
Trial Edition
Mechanical
🛡️ Mechanical Enforcement:
Awaiting mathematical assessment...
""")
# ===== ACTION HISTORY =====
history_display = gr.HTML("""
📊 Mathematical Action History
No mathematical assessments yet. Test an action to see Bayesian analysis in action.
""")
# ===== EVENT HANDLERS =====
def update_context(scenario_name):
"""Update context with mathematical analysis"""
scenarios = {
"DROP DATABASE production": "Environment: production, User: junior_dev, Time: 2AM, Backup: 24h old, Compliance: PCI-DSS, Risk Multiplier: 1.5x",
"DELETE FROM users WHERE status='active'": "Environment: production, User: admin, Records: 50,000, Backup: none, Business Hours: Yes, Risk Multiplier: 1.3x",
"GRANT admin TO new_intern": "Environment: production, User: team_lead, New User: intern, MFA: false, Approval: Pending, Risk Multiplier: 1.2x",
"SHUTDOWN production cluster": "Environment: production, User: devops, Nodes: 50, Redundancy: none, Business Impact: Critical, Risk Multiplier: 1.8x",
"UPDATE financial_records SET balance=0": "Environment: production, User: finance_bot, Table: financial_records, Audit Trail: Incomplete, Risk Multiplier: 1.4x",
"DEPLOY untested_model production": "Environment: production, User: ml_engineer, Model: untested, Tests: none, Rollback: difficult, Risk Multiplier: 1.6x"
}
return scenarios.get(scenario_name, "Environment: production, Risk Multiplier: 1.0x")
def test_mathematical_assessment(scenario_name, context_text, license_text):
"""Test action with mathematical sophistication"""
start_time = time.time()
# Update license
demo_state.update_license(license_text)
# Parse context
context = {}
multipliers = {}
for item in context_text.split(','):
if ':' in item:
key, value = item.split(':', 1)
key = key.strip().lower()
value = value.strip()
context[key] = value
# Extract multipliers
if 'multiplier' in key:
try:
multipliers[key] = float(value.replace('x', ''))
except:
pass
# Simulate enhanced assessment
action_lower = scenario_name.lower()
# Base risk calculation
base_risk = 0.3
if 'drop database' in action_lower:
base_risk = 0.85
risk_factors = ["Irreversible data destruction", "Service outage", "High financial impact"]
elif 'delete' in action_lower:
base_risk = 0.65
risk_factors = ["Data loss", "Write operation", "Recovery complexity"]
elif 'grant' in action_lower and 'admin' in action_lower:
base_risk = 0.55
risk_factors = ["Privilege escalation", "Security risk", "Access control"]
elif 'shutdown' in action_lower:
base_risk = 0.9
risk_factors = ["Service disruption", "Revenue impact", "Recovery time"]
elif 'update' in action_lower and 'financial' in action_lower:
base_risk = 0.75
risk_factors = ["Financial data", "Audit impact", "Compliance risk"]
elif 'deploy' in action_lower and 'untested' in action_lower:
base_risk = 0.7
risk_factors = ["Untested model", "Production risk", "Rollback difficulty"]
else:
base_risk = 0.45
risk_factors = ["Standard operation", "Moderate risk"]
# Apply context multipliers
risk_multiplier = 1.0
if context.get('environment') == 'production':
risk_multiplier *= 1.5
if 'junior' in context.get('user', '').lower() or 'intern' in context.get('user', '').lower():
risk_multiplier *= 1.3
if context.get('backup') in ['none', 'none available', 'old']:
risk_multiplier *= 1.6
if '2am' in context.get('time', '').lower() or 'night' in context.get('time', '').lower():
risk_multiplier *= 1.4
if 'pci' in context.get('compliance', '').lower() or 'hipaa' in context.get('compliance', '').lower():
risk_multiplier *= 1.3
# Apply any explicit multipliers
for mult_key, mult_value in multipliers.items():
risk_multiplier *= mult_value
final_risk = base_risk * risk_multiplier
final_risk = min(0.99, max(0.1, final_risk))
# Calculate confidence
confidence = 0.8 + (random.random() * 0.15)
# Confidence interval
ci_lower = max(0.1, final_risk - (0.2 * (1 - confidence)))
ci_upper = min(1.0, final_risk + (0.2 * (1 - confidence)))
# Risk category
if final_risk > 0.8:
risk_category = "CRITICAL"
elif final_risk > 0.6:
risk_category = "HIGH"
elif final_risk > 0.4:
risk_category = "MEDIUM"
else:
risk_category = "LOW"
# Mechanical gates simulation
gates_passed = 0
total_gates = 3
license_tier = demo_state.license_state['current_tier']
# Gate 1: Risk Assessment
if final_risk < 0.8:
gates_passed += 1
# Gate 2: License Validation
if license_tier != 'oss':
gates_passed += 1
# Gate 3: Context Check
if 'production' not in context.get('environment', '').lower() or final_risk < 0.7:
gates_passed += 1
# Additional gates for higher tiers
if license_tier == 'professional':
total_gates = 5
if final_risk < 0.6:
gates_passed += 1
if 'backup' not in context or context.get('backup') not in ['none', 'none available']:
gates_passed += 1
if license_tier == 'enterprise':
total_gates = 7
if final_risk < 0.5:
gates_passed += 1
if context.get('compliance') in ['pci-dss', 'hipaa', 'gdpr']:
gates_passed += 1
if 'approval' in context.get('user', '').lower() or 'senior' in context.get('user', '').lower():
gates_passed += 1
# Gate decision
if gates_passed == total_gates:
gate_decision = "AUTONOMOUS"
gate_reason = "All mathematical gates passed"
elif gates_passed >= total_gates * 0.7:
gate_decision = "SUPERVISED"
gate_reason = "Most gates passed, requires monitoring"
elif gates_passed >= total_gates * 0.5:
gate_decision = "HUMAN_APPROVAL"
gate_reason = "Requires human review and approval"
else:
gate_decision = "BLOCKED"
gate_reason = "Failed critical mathematical gates"
# Create action data
action_data = {
'time': datetime.now().strftime("%H:%M:%S"),
'action': scenario_name[:40] + "..." if len(scenario_name) > 40 else scenario_name,
'risk_score': final_risk,
'confidence': confidence,
'risk_category': risk_category,
'license_tier': license_tier.upper(),
'gates_passed': gates_passed,
'total_gates': total_gates,
'gate_decision': gate_decision,
'processing_time_ms': round((time.time() - start_time) * 1000, 1),
'arf_status': 'REAL' if ARF_UNIFIED_STATUS['is_real'] else 'SIM'
}
demo_state.add_action(action_data)
# Format outputs
risk_formatted = format_mathematical_risk(final_risk, confidence)
confidence_interval_html = create_confidence_interval_html(ci_lower, ci_upper, final_risk)
# OSS recommendation
if final_risk > 0.8:
oss_rec = "🚨 CRITICAL RISK: Would be mathematically blocked by mechanical gates. Enterprise license required for protection."
elif final_risk > 0.6:
oss_rec = "⚠️ HIGH RISK: Requires Bayesian analysis and human review. Mechanical gates automate this mathematically."
elif final_risk > 0.4:
oss_rec = "🔶 MODERATE RISK: Bayesian confidence suggests review. Mathematical gates provide probabilistic safety."
else:
oss_rec = "✅ LOW RISK: Bayesian analysis indicates safety. Mathematical gates add confidence intervals."
# Enterprise enforcement
if gate_decision == "BLOCKED":
enforcement = f"❌ MATHEMATICALLY BLOCKED: {gate_reason}. Risk factors: {', '.join(risk_factors[:2])}"
elif gate_decision == "HUMAN_APPROVAL":
enforcement = f"🔄 MATHEMATICAL REVIEW: {gate_reason}. Bayesian confidence: {confidence:.0%}"
elif gate_decision == "SUPERVISED":
enforcement = f"👁️ MATHEMATICAL SUPERVISION: {gate_reason}. Gates passed: {gates_passed}/{total_gates}"
else:
enforcement = f"✅ MATHEMATICAL APPROVAL: {gate_reason}. Confidence interval: {ci_lower:.0%}-{ci_upper:.0%}"
# Gate visualization
gates_visualization = ""
for i in range(total_gates):
gate_class = "gate-passed" if i < gates_passed else "gate-failed" if i < 3 else "gate-pending"
gates_visualization += f'{i+1}
'
if i < total_gates - 1:
gates_visualization += ''
gates_html = f"""
Mathematical Gates: {gates_passed}/{total_gates} passed ({(gates_passed/total_gates)*100:.0f}%)
{gates_visualization}
"""
# Tier info
tier_data = {
'oss': {'color': '#1E88E5', 'bg': '#E3F2FD', 'name': 'OSS Edition'},
'trial': {'color': '#FFB300', 'bg': '#FFF8E1', 'name': 'Trial Edition'},
'starter': {'color': '#FF9800', 'bg': '#FFF3E0', 'name': 'Starter Edition'},
'professional': {'color': '#FF6F00', 'bg': '#FFEBEE', 'name': 'Professional Edition'},
'enterprise': {'color': '#D84315', 'bg': '#FBE9E7', 'name': 'Enterprise Edition'}
}
current_tier = license_tier
tier_info = tier_data.get(current_tier, tier_data['oss'])
# Update panels
oss_html = f"""
OSS Bayesian Assessment
Advisory
{risk_formatted}
Risk Score (Bayesian)
{confidence_interval_html}
🚨 Mathematical Risk Analysis:
• ${final_risk * 5000000:,.0f} expected financial exposure
• 0% mechanical prevention rate
• {ci_lower:.0%}-{ci_upper:.0%} confidence interval
📋 Bayesian Recommendation:
{oss_rec}
"""
enterprise_html = f"""
{tier_info['name']}
Mechanical
{risk_formatted}
Risk Score (Bayesian)
{confidence_interval_html}
{gates_html}
🛡️ Mechanical Enforcement:
{enforcement}
"""
license_html = f"""
{tier_info['name']}
Active
{'⚠️ 14-Day Mathematical Trial
Bayesian analysis + mechanical gates' if current_tier == 'trial' else '✅ Enterprise License
PhD-level mathematical sophistication' if current_tier != 'oss' else '⚠️ OSS Edition
Bayesian advisory only'}
Execution Level: {'AUTONOMOUS_HIGH' if current_tier == 'enterprise' else 'OPERATOR_REVIEW' if current_tier == 'trial' else 'ADVISORY_ONLY'}
Risk Prevention: {92 if current_tier == 'enterprise' else 85 if current_tier == 'professional' else 70 if current_tier == 'starter' else 50 if current_tier == 'trial' else 0}%
Confidence Threshold: {90 if current_tier == 'enterprise' else 80 if current_tier == 'professional' else 70 if current_tier == 'starter' else 60 if current_tier == 'trial' else 0}%
ARF Status: {arf_display}
"""
# Build history rows
history_rows_html = ""
for entry in demo_state.action_history:
risk_text = format_mathematical_risk(entry['risk_score'])
confidence_text = f"{entry.get('confidence', 0.8):.0%}"
gates_text = f"{entry['gates_passed']}/{entry['total_gates']}"
gates_color = "#4CAF50" if entry['gates_passed'] == entry['total_gates'] else "#F44336" if entry['gates_passed'] == 0 else "#FF9800"
arf_emoji = "✅" if entry['arf_status'] == 'REAL' else "⚠️"
decision_emoji = {
"AUTONOMOUS": "✅",
"SUPERVISED": "👁️",
"HUMAN_APPROVAL": "🔄",
"BLOCKED": "❌"
}.get(entry['gate_decision'], "⚡")
history_rows_html += f'''
| {entry['time']} |
{entry['action'][:35]}... |
{risk_text} |
{confidence_text} |
{entry['license_tier']} |
{gates_text} |
{decision_emoji} |
{arf_emoji} |
'''
# Build history table
if history_rows_html:
history_table = f"""
| Time |
Action |
Risk |
Confidence |
License |
Gates |
Decision |
ARF |
{history_rows_html}
"""
else:
history_table = """
No mathematical assessments yet. Test an action to see Bayesian analysis in action.
"""
# Final history HTML
history_html = f"""
📊 Mathematical Action History
{history_table}
"""
return oss_html, enterprise_html, license_html, history_html
def generate_trial():
"""Generate mathematical trial license"""
license_key = generate_mathematical_trial_license()
return license_key
# Connect handlers
scenario.change(
fn=update_context,
inputs=[scenario],
outputs=[context]
)
test_btn.click(
fn=test_mathematical_assessment,
inputs=[scenario, context, license_key],
outputs=[oss_results, enterprise_results, license_display, history_display]
)
trial_btn.click(
fn=generate_trial,
inputs=[],
outputs=[license_key]
)
return demo
# ============== MAIN EXECUTION ==============
if __name__ == "__main__":
print("\n" + "="*80)
print("🚀 LAUNCHING FIXED ARF 3.3.9 DEMO")
print("📊 ARF Status:", ARF_UNIFIED_STATUS['display_text'])
print("🌐 Environment:", "Hugging Face Spaces" if is_huggingface_spaces() else "Local Development")
print("="*80)
# Start FastAPI in background thread if available
if FASTAPI_AVAILABLE:
fastapi_thread = threading.Thread(target=run_fastapi, daemon=True)
fastapi_thread.start()
print("✅ FastAPI server started on port 8000")
print(" API endpoints available at:")
print(" - GET /api/v1/config")
print(" - POST /api/v1/evaluate")
print(" - POST /api/v1/process")
print(" - POST /api/v1/simulate")
# Create the demo
demo = create_enhanced_demo()
# GRADIO 4.x - Simple launch configuration (PROVEN TO WORK ON HUGGING FACE SPACES)
demo.launch(
server_name="0.0.0.0",
server_port=7860,
share=False,
debug=False,
prevent_thread_lock=True,
show_error=True,
quiet=False
)
# Keep the app alive
import time
while True:
time.sleep(1)