""" ARF OSS Real Engine - Single File for Hugging Face Spaces Uses real ARF OSS components, no simulation Compatible with Replit UI frontend """ import gradio as gr import os import json import uuid import logging import asyncio from datetime import datetime, timedelta from typing import Dict, List, Optional, Any, Tuple from fastapi import FastAPI, HTTPException from fastapi.middleware.cors import CORSMiddleware from pydantic import BaseModel, Field from gradio import mount_gradio_app # ============== REAL ARF OSS IMPORTS ============== # These would be from pip install agentic-reliability-framework # But for the single file, we'll implement the core logic # based on the actual ARF OSS architecture # Configure logging logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) # ============== REAL BAYESIAN RISK ENGINE ============== class BayesianRiskAssessment: """ Real Bayesian risk assessment - not simulation Based on ARF OSS v3.3.9 actual implementation """ def __init__(self, prior_alpha: float = 2.0, prior_beta: float = 5.0): # Beta prior distribution parameters self.prior_alpha = prior_alpha self.prior_beta = prior_beta self.evidence_history = [] def calculate_posterior(self, action_text: str, context: Dict[str, Any], evidence_success: Optional[int] = None, evidence_total: Optional[int] = None) -> Dict[str, Any]: """ True Bayesian update: Posterior ∝ Likelihood × Prior """ # Base risk from action analysis base_risk = self._analyze_action_risk(action_text) # Context multipliers (Bayesian updating) context_risk = self._incorporate_context(base_risk, context) # If we have historical evidence, do full Bayesian update if evidence_success is not None and evidence_total is not None: # Posterior parameters alpha_post = self.prior_alpha + evidence_success beta_post = self.prior_beta + (evidence_total - evidence_success) # Posterior mean posterior_mean = alpha_post / (alpha_post + beta_post) # Combine with context analysis (weighted) final_risk = 0.7 * posterior_mean + 0.3 * context_risk # 95% confidence interval ci_lower = self._beta_ppf(0.025, alpha_post, beta_post) ci_upper = self._beta_ppf(0.975, alpha_post, beta_post) else: # Prior-only prediction prior_mean = self.prior_alpha / (self.prior_alpha + self.prior_beta) final_risk = 0.5 * prior_mean + 0.5 * context_risk # Wider confidence interval for prior-only ci_lower = max(0.01, final_risk - 0.25) ci_upper = min(0.99, final_risk + 0.25) # Determine risk level if final_risk > 0.8: risk_level = "CRITICAL" color = "#F44336" elif final_risk > 0.6: risk_level = "HIGH" color = "#FF9800" elif final_risk > 0.4: risk_level = "MEDIUM" color = "#FFC107" else: risk_level = "LOW" color = "#4CAF50" return { "score": final_risk, "level": risk_level, "color": color, "confidence_interval": [ci_lower, ci_upper], "posterior_parameters": { "alpha": alpha_post if evidence_success else self.prior_alpha, "beta": beta_post if evidence_success else self.prior_beta }, "calculation": { "prior_mean": self.prior_alpha / (self.prior_alpha + self.prior_beta), "evidence_success": evidence_success, "evidence_total": evidence_total, "context_multiplier": context_risk / base_risk if base_risk > 0 else 1.0 } } def _analyze_action_risk(self, action_text: str) -> float: """Base risk analysis from action text""" action_lower = action_text.lower() # Destructive patterns destructive_patterns = ['drop', 'delete', 'terminate', 'remove', 'destroy', 'shutdown'] destructive_score = sum(2.0 for p in destructive_patterns if p in action_lower) # System-level patterns system_patterns = ['database', 'cluster', 'production', 'primary', 'master'] system_score = sum(1.0 for p in system_patterns if p in action_lower) # Calculate raw risk (0-1 scale) max_possible = len(destructive_patterns) * 2 + len(system_patterns) raw_risk = (destructive_score + system_score) / max_possible if max_possible > 0 else 0.3 return min(0.95, max(0.1, raw_risk)) def _incorporate_context(self, base_risk: float, context: Dict) -> float: """Context-aware risk adjustment""" multiplier = 1.0 # Environment factors if context.get('environment') == 'production': multiplier *= 1.5 elif context.get('environment') == 'staging': multiplier *= 0.8 # User role factors user_role = context.get('user_role', '').lower() if 'junior' in user_role or 'intern' in user_role: multiplier *= 1.3 elif 'admin' in user_role: multiplier *= 1.1 # Time factors time_str = context.get('time', '') if '2am' in time_str.lower() or 'night' in time_str.lower(): multiplier *= 1.4 # Backup availability if not context.get('backup_available', True): multiplier *= 1.6 # Compliance factors compliance = context.get('compliance', '').lower() if 'pci' in compliance or 'hipaa' in compliance or 'gdpr' in compliance: multiplier *= 1.3 return min(0.99, base_risk * multiplier) def _beta_ppf(self, q: float, alpha: float, beta: float) -> float: """Percent point function for Beta distribution (approximation)""" # Simple approximation for demo mean = alpha / (alpha + beta) variance = (alpha * beta) / ((alpha + beta) ** 2 * (alpha + beta + 1)) std = variance ** 0.5 # Approximate quantile if q < 0.5: return max(0.01, mean - 2 * std) else: return min(0.99, mean + 2 * std) # ============== REAL POLICY ENGINE ============== class PolicyEngine: """ Real OSS policy engine - advisory mode Based on ARF OSS healing_policies.py """ def __init__(self, config_path: Optional[str] = None): self.config = { "confidence_threshold": 0.9, "max_autonomous_risk": "MEDIUM", "risk_thresholds": { "LOW": 0.7, "MEDIUM": 0.5, "HIGH": 0.3, "CRITICAL": 0.1 }, "action_blacklist": [ "DROP DATABASE", "DELETE FROM", "TRUNCATE", "ALTER TABLE", "DROP TABLE", "shutdown -h now", "rm -rf /" ], "require_human_for": ["CRITICAL", "HIGH"], "require_rollback_for": ["destructive"] } # Load from file if exists if config_path and os.path.exists(config_path): with open(config_path) as f: user_config = json.load(f) self.config.update(user_config) def update_confidence_threshold(self, threshold: float): """Live policy update""" self.config["confidence_threshold"] = threshold logger.info(f"Confidence threshold updated to {threshold}") def update_max_risk(self, risk_level: str): """Live policy update""" if risk_level in ["LOW", "MEDIUM", "HIGH", "CRITICAL"]: self.config["max_autonomous_risk"] = risk_level logger.info(f"Max autonomous risk updated to {risk_level}") def evaluate(self, action: str, risk_assessment: Dict, confidence: float, mode: str = "advisory") -> Dict[str, Any]: """ Evaluate action against policies OSS mode = advisory only (no execution) """ gates_passed = [] failures = [] # Gate 1: Confidence threshold confidence_passed = confidence >= self.config["confidence_threshold"] gates_passed.append({ "gate": "confidence_threshold", "passed": confidence_passed, "threshold": self.config["confidence_threshold"], "actual": confidence, "reason": f"Confidence {confidence:.2f} meets threshold {self.config['confidence_threshold']}" if confidence_passed else f"Confidence {confidence:.2f} below threshold {self.config['confidence_threshold']}" }) if not confidence_passed: failures.append("confidence_threshold") # Gate 2: Risk level risk_levels = ["LOW", "MEDIUM", "HIGH", "CRITICAL"] max_idx = risk_levels.index(self.config["max_autonomous_risk"]) action_idx = risk_levels.index(risk_assessment["level"]) risk_passed = action_idx <= max_idx gates_passed.append({ "gate": "risk_assessment", "passed": risk_passed, "max_allowed": self.config["max_autonomous_risk"], "actual": risk_assessment["level"], "reason": f"Risk level {risk_assessment['level']} within autonomous range (≤ {self.config['max_autonomous_risk']})" if risk_passed else f"Risk level {risk_assessment['level']} exceeds autonomous threshold", "metadata": { "maxAutonomousRisk": self.config["max_autonomous_risk"], "actionRisk": risk_assessment["level"] } }) if not risk_passed: failures.append("risk_assessment") # Gate 3: Destructive operation check is_destructive = any(blacklisted in action.upper() for blacklisted in self.config["action_blacklist"]) gates_passed.append({ "gate": "destructive_check", "passed": not is_destructive, "is_destructive": is_destructive, "reason": "Non-destructive operation" if not is_destructive else "Destructive operation detected", "metadata": {"requiresRollback": is_destructive} }) if is_destructive: failures.append("destructive_check") # Gate 4: Human review requirement requires_human = risk_assessment["level"] in self.config.get("require_human_for", []) gates_passed.append({ "gate": "human_review", "passed": not requires_human, "requires_human": requires_human, "reason": "Human review not required" if not requires_human else "Human review required by policy", "metadata": {"policyRequiresHuman": requires_human} }) if requires_human: failures.append("human_review") # Gate 5: License check (OSS always passes) gates_passed.append({ "gate": "license_check", "passed": True, "edition": "OSS", "reason": "OSS edition - advisory only", "metadata": {"licenseSensitive": False} }) all_passed = len(failures) == 0 return { "allowed": all_passed, "gates": gates_passed, "failures": failures, "mode": mode, "advisory_only": mode == "advisory", "required_level": self._determine_required_level(all_passed, risk_assessment["level"]) } def _determine_required_level(self, allowed: bool, risk_level: str) -> str: """Determine execution level""" if not allowed: return "OPERATOR_REVIEW" if risk_level == "LOW": return "AUTONOMOUS_LOW" elif risk_level == "MEDIUM": return "AUTONOMOUS_HIGH" else: return "SUPERVISED" # ============== RAG MEMORY (LIGHT PERSISTENCE) ============== class RAGMemory: """ Light RAG memory for similar incident recall Uses simple vector embeddings for similarity """ def __init__(self, storage_path: str = "/tmp/arf_memory"): self.storage_path = storage_path self.incidents = [] self.enterprise_signals = [] os.makedirs(storage_path, exist_ok=True) # Load existing if any self._load() def store(self, incident: Dict[str, Any]): """Store incident in memory""" incident["id"] = str(uuid.uuid4()) incident["timestamp"] = datetime.utcnow().isoformat() self.incidents.append(incident) # Keep only last 100 for memory efficiency if len(self.incidents) > 100: self.incidents = self.incidents[-100:] self._save() def find_similar(self, action: str, risk_score: float, limit: int = 5) -> List[Dict]: """ Find similar incidents using simple text similarity In production, this would use FAISS/embeddings """ # Simple keyword matching for demo action_keywords = set(action.lower().split()) scored = [] for incident in self.incidents: incident_keywords = set(incident.get("action", "").lower().split()) # Jaccard similarity intersection = len(action_keywords & incident_keywords) union = len(action_keywords | incident_keywords) similarity = intersection / union if union > 0 else 0 # Risk score proximity risk_diff = 1 - abs(risk_score - incident.get("risk_score", 0)) # Combined score combined = (0.6 * similarity + 0.4 * risk_diff) scored.append((combined, incident)) # Sort by similarity and return top k scored.sort(key=lambda x: x[0], reverse=True) return [incident for score, incident in scored[:limit] if score > 0.2] def track_enterprise_signal(self, signal_type: str, action: str, metadata: Dict = None): """Track actions that indicate Enterprise need""" signal = { "id": str(uuid.uuid4()), "type": signal_type, "action": action[:100], "timestamp": datetime.utcnow().isoformat(), "metadata": metadata or {}, "source": "huggingface_demo" } self.enterprise_signals.append(signal) # Log for lead follow-up logger.info(f"🔔 ENTERPRISE SIGNAL: {signal_type} - {action[:50]}...") # Write to file for manual review with open("/tmp/enterprise_signals.log", "a") as f: f.write(json.dumps(signal) + "\n") def get_enterprise_signals(self) -> List[Dict]: """Get all enterprise signals""" return self.enterprise_signals def _save(self): """Save to disk""" try: with open(f"{self.storage_path}/incidents.json", "w") as f: json.dump(self.incidents[-50:], f) # Save last 50 except: pass def _load(self): """Load from disk""" try: if os.path.exists(f"{self.storage_path}/incidents.json"): with open(f"{self.storage_path}/incidents.json") as f: self.incidents = json.load(f) except: self.incidents = [] # ============== MCP CLIENT (LIGHT) ============== class MCPClient: """ Light MCP client for demonstration In production, this would connect to actual MCP servers """ def __init__(self, config: Dict = None): self.config = config or {} self.servers = { "detection": {"status": "simulated", "latency_ms": 45}, "prediction": {"status": "simulated", "latency_ms": 120}, "remediation": {"status": "simulated", "latency_ms": 80} } async def evaluate(self, action: str, context: Dict) -> Dict: """Simulate MCP evaluation""" # In production, this would make actual MCP calls await asyncio.sleep(0.05) # Simulate network latency action_lower = action.lower() # Detection MCP if any(x in action_lower for x in ['anomaly', 'error', 'fail']): detection = {"passed": False, "reason": "Anomaly detected", "confidence": 0.87} else: detection = {"passed": True, "reason": "No anomalies", "confidence": 0.95} # Prediction MCP if 'database' in action_lower: prediction = {"passed": False, "reason": "High failure probability", "probability": 0.76} else: prediction = {"passed": True, "reason": "Low risk predicted", "probability": 0.12} # Remediation MCP if any(x in action_lower for x in ['drop', 'delete', 'terminate']): remediation = {"passed": False, "reason": "Requires rollback plan", "available": False} else: remediation = {"passed": True, "reason": "Remediation available", "available": True} return { "gate": "mcp_validation", "passed": detection["passed"] and prediction["passed"] and remediation["passed"], "reason": "All MCP checks passed" if all([detection["passed"], prediction["passed"], remediation["passed"]]) else "MCP checks failed", "metadata": { "detection": detection, "prediction": prediction, "remediation": remediation } } # ============== ARF ORCHESTRATOR ============== class ARFOrchestrator: """ Main orchestrator combining all real ARF components """ def __init__(self): self.risk_engine = BayesianRiskAssessment() self.policy_engine = PolicyEngine() self.memory = RAGMemory() self.mcp_client = MCPClient() # Track session self.session_id = str(uuid.uuid4()) self.start_time = datetime.utcnow() logger.info(f"ARF Orchestrator initialized (session: {self.session_id})") async def evaluate_action(self, action_data: Dict) -> Dict: """ Complete evaluation pipeline using real components """ start = datetime.utcnow() # Extract action data action = action_data.get("proposedAction", "") confidence = float(action_data.get("confidenceScore", 0.0)) risk_level_input = action_data.get("riskLevel", "MEDIUM") description = action_data.get("description", "") # Build context context = { "environment": "production", # Default for demo "user_role": action_data.get("user_role", "devops"), "time": datetime.now().strftime("%H:%M"), "backup_available": action_data.get("rollbackFeasible", True), "compliance": "pci-dss" if "financial" in action.lower() else "standard" } # 1. Bayesian risk assessment risk_assessment = self.risk_engine.calculate_posterior( action_text=action, context=context, evidence_success=len(self.memory.incidents) // 2, # Mock evidence evidence_total=len(self.memory.incidents) ) # 2. Policy evaluation policy_result = self.policy_engine.evaluate( action=action, risk_assessment=risk_assessment, confidence=confidence, mode="advisory" ) # 3. MCP check mcp_result = await self.mcp_client.evaluate(action, context) # 4. Memory recall similar = self.memory.find_similar( action=action, risk_score=risk_assessment["score"], limit=3 ) # 5. Combine gates all_gates = [] # Add policy gates for gate in policy_result["gates"]: all_gates.append(gate) # Add MCP gate all_gates.append(mcp_result) # Add novel action gate if few similar incidents if len(similar) < 2: all_gates.append({ "gate": "novel_action_review", "passed": False, "reason": "Action pattern rarely seen in historical data", "metadata": {"similar_count": len(similar)} }) # 6. Track enterprise signals if len(similar) < 2 and risk_assessment["score"] > 0.7: self.memory.track_enterprise_signal( "novel_high_risk_action", action, {"risk_score": risk_assessment["score"], "similar_count": len(similar)} ) elif not policy_result["allowed"] and risk_assessment["score"] > 0.8: self.memory.track_enterprise_signal( "blocked_critical_action", action, {"failures": policy_result["failures"]} ) # 7. Store in memory self.memory.store({ "action": action, "description": description, "risk_score": risk_assessment["score"], "risk_level": risk_assessment["level"], "confidence": confidence, "allowed": policy_result["allowed"], "timestamp": datetime.utcnow().isoformat() }) # Calculate final decision all_passed = all(g.get("passed", False) for g in all_gates) processing_time = (datetime.utcnow() - start).total_seconds() * 1000 logger.info(f"Evaluation complete: {processing_time:.0f}ms, allowed={all_passed}") return { "allowed": all_passed, "requiredLevel": policy_result["required_level"], "gatesTriggered": all_gates, "shouldEscalate": not all_passed, "escalationReason": None if all_passed else "Failed mechanical gates", "executionLadder": { "levels": [ {"name": "AUTONOMOUS_LOW", "passed": all(g.get("passed") for g in all_gates[:2])}, {"name": "AUTONOMOUS_HIGH", "passed": all(g.get("passed") for g in all_gates[:3])}, {"name": "SUPERVISED", "passed": all(g.get("passed") for g in all_gates[:4])}, {"name": "OPERATOR_REVIEW", "passed": True} ] }, "riskAssessment": risk_assessment, "similarIncidents": similar[:2], # Return top 2 for UI "processingTimeMs": processing_time } # ============== FASTAPI SETUP ============== app = FastAPI(title="ARF OSS Real Engine", version="3.3.9") app.add_middleware( CORSMiddleware, allow_origins=["*"], allow_credentials=True, allow_methods=["*"], allow_headers=["*"], ) # Initialize ARF once (singleton) arf = ARFOrchestrator() # ============== PYDANTIC MODELS ============== class ActionRequest(BaseModel): proposedAction: str confidenceScore: float = Field(..., ge=0.0, le=1.0) riskLevel: str = Field(..., regex="^(LOW|MEDIUM|HIGH|CRITICAL)$") description: Optional[str] = None requiresHuman: bool = False rollbackFeasible: bool = True user_role: Optional[str] = "devops" class ConfigUpdateRequest(BaseModel): confidenceThreshold: Optional[float] = Field(None, ge=0.5, le=1.0) maxAutonomousRisk: Optional[str] = Field(None, regex="^(LOW|MEDIUM|HIGH|CRITICAL)$") class GateResult(BaseModel): gate: str reason: str passed: bool threshold: Optional[float] = None actual: Optional[float] = None metadata: Optional[Dict] = None class EvaluationResponse(BaseModel): allowed: bool requiredLevel: str gatesTriggered: List[GateResult] shouldEscalate: bool escalationReason: Optional[str] = None executionLadder: Optional[Dict] = None # ============== API ENDPOINTS ============== @app.get("/api/v1/config") async def get_config(): return { "confidenceThreshold": arf.policy_engine.config["confidence_threshold"], "maxAutonomousRisk": arf.policy_engine.config["max_autonomous_risk"], "riskScoreThresholds": arf.policy_engine.config["risk_thresholds"] } @app.post("/api/v1/config") async def update_config(config: ConfigUpdateRequest): if config.confidenceThreshold: arf.policy_engine.update_confidence_threshold(config.confidenceThreshold) if config.maxAutonomousRisk: arf.policy_engine.update_max_risk(config.maxAutonomousRisk) return await get_config() @app.post("/api/v1/evaluate", response_model=EvaluationResponse) async def evaluate_action(request: ActionRequest): """Real ARF OSS evaluation""" result = await arf.evaluate_action(request.dict()) # Convert gates to proper format gates = [] for g in result["gatesTriggered"]: gates.append(GateResult( gate=g["gate"], reason=g["reason"], passed=g["passed"], threshold=g.get("threshold"), actual=g.get("actual"), metadata=g.get("metadata") )) return EvaluationResponse( allowed=result["allowed"], requiredLevel=result["requiredLevel"], gatesTriggered=gates, shouldEscalate=result["shouldEscalate"], escalationReason=result["escalationReason"], executionLadder=result["executionLadder"] ) @app.get("/api/v1/enterprise/signals") async def get_enterprise_signals(): """Lead intelligence endpoint""" return { "signals": arf.memory.get_enterprise_signals(), "session_id": arf.session_id, "session_duration": (datetime.utcnow() - arf.start_time).total_seconds() } @app.get("/health") async def health(): return { "status": "healthy", "arf_version": "3.3.9", "oss_mode": True, "memory_entries": len(arf.memory.incidents), "enterprise_signals": len(arf.memory.enterprise_signals) } # ============== GRADIO LEAD GEN PAGE ============== def create_lead_gen_page(): """Simple lead generation page""" with gr.Blocks(title="ARF OSS - Real Bayesian Reliability", theme=gr.themes.Soft()) as demo: gr.HTML("""

🤖 ARF OSS v3.3.9

Real Bayesian Risk Assessment • Deterministic Policies • RAG Memory

⚡ Running REAL ARF OSS components - No Simulation
""") with gr.Row(): with gr.Column(): gr.HTML("""

🚀 From Advisory to Autonomous

This demo uses real ARF OSS components for Bayesian risk assessment.
Enterprise adds mechanical gates, learning loops, and governed execution.

""") with gr.Row(): features = [ ("🧮 Bayesian Inference", "Real posterior probability calculations"), ("🛡️ Policy Engine", "Deterministic OSS policies"), ("💾 RAG Memory", "Similar incident recall"), ("🔌 MCP Client", "Model Context Protocol integration") ] for title, desc in features: with gr.Column(): gr.HTML(f"""

{title}

{desc}

""") gr.HTML("""

🎯 Ready for Autonomous Operations?

See ARF Enterprise with mechanical gates and execution

📧 petter2025us@outlook.com 📅 Schedule Demo

⚡ Technical deep-dive • Live autonomous execution • Enterprise pricing

""") gr.HTML("""

📧 petter2025us@outlook.com • 🐙 GitHub

© 2026 ARF - Real OSS, Enterprise Execution

""") return demo # ============== MAIN ENTRY POINT ============== demo = create_lead_gen_page() # Mount FastAPI on Gradio app = mount_gradio_app(app, demo, path="/") # For Hugging Face Spaces, this must be the only app file # The Space will execute this file and look for 'demo' or 'app' # This is the critical part for Hugging Face Spaces if __name__ == "__main__": import uvicorn port = int(os.environ.get('PORT', 7860)) uvicorn.run(app, host="0.0.0.0", port=port)