app.py

# hf_demo.py – ARF v4 API with Gradio frontend (optional)
import logging
import uuid
from datetime import datetime, timezone
from typing import Dict, Any, Optional

from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
import gradio as gr

# ARF v4 imports
from agentic_reliability_framework.core.governance.risk_engine import RiskEngine
from agentic_reliability_framework.runtime.memory import create_faiss_index, RAGGraphMemory
from agentic_reliability_framework.runtime.memory.constants import MemoryConstants

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# ========================= FASTAPI APP =========================
app = FastAPI(title="ARF v4 API")

# Enable CORS for your frontend
app.add_middleware(
    CORSMiddleware,
    allow_origins=["https://arf-frontend-sandy.vercel.app"],
    allow_methods=["*"],
    allow_headers=["*"],
)

# ========================= ARF COMPONENTS =========================
risk_engine = RiskEngine()
faiss_index = create_faiss_index(dim=MemoryConstants.VECTOR_DIM)
memory = RAGGraphMemory(faiss_index)

# In‑memory storage for demo purposes (replace with a real DB later)
decision_history = []

# ========================= PYDANTIC MODELS =========================
class EvaluateRequest(BaseModel):
    service_name: str
    event_type: str
    severity: str
    metrics: Dict[str, float] = {}

class EvaluateResponse(BaseModel):
    risk_score: float
    base_risk: float
    memory_risk: Optional[float] = None
    weight: float
    similar_events: list = []
    confidence: float

# ========================= HELPER: Demo Intent =========================
class _DemoIntent:
    environment = "dev"
    deployment_target = "dev"
    service_name = "demo"

# ========================= API ENDPOINTS =========================

@app.get("/")
async def root():
    """Root endpoint – returns a welcome message."""
    return {"message": "ARF v4 API. See /docs for documentation."}

@app.get("/health")
async def health():
    return {"status": "ok", "version": "4.0.0"}

@app.get("/api/v1/get_risk")
async def get_risk():
    """Return the current demo risk."""
    intent = _DemoIntent()
    risk_value, explanation, contributions = risk_engine.calculate_risk(
        intent=intent,
        cost_estimate=None,
        policy_violations=[],
    )
    decision = "approve"
    if risk_value > 0.8:
        decision = "deny"
    elif risk_value > 0.2:
        decision = "escalate"

    decision_id = str(uuid.uuid4())
    decision_history.append({
        "decision_id": decision_id,
        "timestamp": datetime.now(timezone.utc).isoformat(),
        "risk_score": float(risk_value),
        "outcome": None,
    })

    return {
        "system_risk": float(risk_value),
        "status": "critical" if risk_value > 0.8 else "normal",
        "explanation": explanation,
        "contributions": contributions,
        "decision_id": decision_id,
        "decision": decision,
        "timestamp": datetime.now(timezone.utc).isoformat()
    }

@app.get("/api/v1/history")
async def get_history():
    """Return the last 10 decisions."""
    return decision_history[-10:]

@app.post("/api/v1/incidents/evaluate", response_model=EvaluateResponse)
async def evaluate_incident(request: EvaluateRequest):
    """
    Evaluate an incident and return a risk score with explainability.
    This is a placeholder – replace with actual call to your risk engine.
    """
    return EvaluateResponse(
        risk_score=0.23,
        base_risk=0.15,
        memory_risk=0.3,
        weight=0.5,
        similar_events=[],
        confidence=0.9
    )

@app.post("/api/v1/feedback")
async def record_outcome(decision_id: str, success: bool):
    """Record the outcome of a decision (success/failure)."""
    for dec in decision_history:
        if dec["decision_id"] == decision_id:
            dec["outcome"] = "success" if success else "failure"
            intent = _DemoIntent()
            try:
                risk_engine.update_outcome(intent, success)
            except Exception as e:
                logger.exception("Outcome update failed")
            return {"status": "ok", "decision_id": decision_id, "outcome": dec["outcome"]}
    return {"error": "decision not found"}

# ========================= GRADIO UI (Optional) =========================
# Mount the Gradio interface at the root path.

def get_risk_snapshot():
    try:
        intent = _DemoIntent()
        risk_value, explanation, contributions = risk_engine.calculate_risk(
            intent=intent,
            cost_estimate=None,
            policy_violations=[],
        )
        decision = "approve"
        if risk_value > 0.8:
            decision = "deny"
        elif risk_value > 0.2:
            decision = "escalate"
        decision_id = str(uuid.uuid4())
        decision_history.append({
            "decision_id": decision_id,
            "timestamp": datetime.now(timezone.utc).isoformat(),
            "risk_score": float(risk_value),
            "outcome": None,
        })
        return {
            "risk": float(risk_value),
            "status": "critical" if risk_value > 0.8 else "normal",
            "explanation": explanation,
            "contributions": contributions,
            "decision_id": decision_id,
            "decision": decision,
            "timestamp": datetime.now(timezone.utc).isoformat()
        }
    except Exception as e:
        logger.exception("Failed to compute risk snapshot")
        return {"error": str(e)}

def get_health_snapshot():
    return {"status": "ok", "version": "4.0.0", "service": "ARF OSS API", "timestamp": datetime.now(timezone.utc).isoformat()}

def get_memory_snapshot():
    if memory.has_historical_data():
        return {"status": "ok", "memory_stats": memory.get_graph_stats(), "timestamp": datetime.now(timezone.utc).isoformat()}
    return {"status": "empty", "memory_stats": "No historical memory yet.", "timestamp": datetime.now(timezone.utc).isoformat()}

def record_outcome_ui(success: bool):
    if not decision_history:
        return {"error": "no decisions yet"}
    last = decision_history[-1]
    last["outcome"] = "success" if success else "failure"
    intent = _DemoIntent()
    try:
        risk_engine.update_outcome(intent, success)
    except Exception as e:
        logger.exception("Outcome update failed")
    return {"decision_id": last["decision_id"], "outcome": last["outcome"], "timestamp": datetime.now(timezone.utc).isoformat()}

with gr.Blocks(title="ARF v4 Demo") as demo:
    gr.Markdown("# Agentic Reliability Framework v4\n### Probabilistic Infrastructure Governance")
    with gr.Row():
        health_output = gr.JSON(label="Health")
        risk_output = gr.JSON(label="Current Risk")
    with gr.Row():
        memory_output = gr.JSON(label="Memory Stats")
    with gr.Row():
        decision_output = gr.JSON(label="Recent Decisions")
    with gr.Row():
        refresh_btn = gr.Button("Evaluate Intent")
        success_btn = gr.Button("Action Succeeded")
        fail_btn = gr.Button("Action Failed")
    refresh_btn.click(fn=get_risk_snapshot, outputs=risk_output)
    success_btn.click(fn=lambda: record_outcome_ui(True), outputs=decision_output)
    fail_btn.click(fn=lambda: record_outcome_ui(False), outputs=decision_output)
    with gr.Row():
        health_btn = gr.Button("Refresh Health")
        memory_btn = gr.Button("Refresh Memory")
        history_btn = gr.Button("Show Decision History")
    health_btn.click(fn=get_health_snapshot, outputs=health_output)
    memory_btn.click(fn=get_memory_snapshot, outputs=memory_output)
    history_btn.click(fn=lambda: decision_history[-10:], outputs=decision_output)

# Mount Gradio app at the root path – the Hugging Face platform will serve this FastAPI app.
app = gr.mount_gradio_app(app, demo, path="/")