app.py

import gradio as gr
import asyncio
import json
import logging
import traceback
import random
from datetime import datetime

# Import the base engine
from agentic_reliability_framework.runtime.engine import EnhancedReliabilityEngine

# Import our new AI components
from ai_event import AIEvent
from hallucination_detective import HallucinationDetectiveAgent
from memory_drift_diagnostician import MemoryDriftDiagnosticianAgent

logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)

# Initialize the engine (for infrastructure analysis)
try:
    logger.info("Initializing EnhancedReliabilityEngine...")
    engine = EnhancedReliabilityEngine()
    logger.info("Engine initialized successfully.")
except Exception as e:
    logger.error(f"Failed to initialize engine: {e}\n{traceback.format_exc()}")
    engine = None

# AI agents (initialize once)
hallucination_detective = HallucinationDetectiveAgent()
memory_drift_diagnostician = MemoryDriftDiagnosticianAgent()

async def analyze_infrastructure(component, latency, error_rate, throughput, cpu_util, memory_util):
    """Original infrastructure analysis."""
    if engine is None:
        return json.dumps({"error": "Engine failed to initialize. Check logs."}, indent=2)
    try:
        result = await engine.process_event_enhanced(
            component=component,
            latency=float(latency),
            error_rate=float(error_rate),
            throughput=float(throughput) if throughput else 1000.0,
            cpu_util=float(cpu_util) if cpu_util else None,
            memory_util=float(memory_util) if memory_util else None
        )
        return json.dumps(result, indent=2)
    except Exception as e:
        logger.error(f"Infrastructure analysis error: {e}\n{traceback.format_exc()}")
        return json.dumps({"error": str(e), "traceback": traceback.format_exc()}, indent=2)

async def analyze_ai(component, prompt, model_name, model_version, confidence, perplexity, retrieval_score):
    """AI reliability analysis."""
    try:
        # Simulate a response (in a real app, call an actual model)
        response = f"Mock response to: {prompt}"
        # Create AIEvent
        event = AIEvent(
            timestamp=datetime.utcnow(),
            component=component,
            service_mesh="ai",
            latency_p99=random.uniform(100, 500),
            error_rate=0.0,
            throughput=1,
            cpu_util=None,
            memory_util=None,
            model_name=model_name,
            model_version=model_version,
            prompt=prompt,
            response=response,
            response_length=len(response),
            confidence=confidence,
            perplexity=perplexity,
            retrieval_scores=[retrieval_score],
            user_feedback=None,
            latency_ms=random.uniform(200, 800)
        )
        # Run agents
        hallu_result = await hallucination_detective.analyze(event)
        drift_result = await memory_drift_diagnostician.analyze(event)
        # Combine results
        result = {
            "hallucination_detection": hallu_result,
            "memory_drift_detection": drift_result,
            "response": response
        }
        return json.dumps(result, indent=2)
    except Exception as e:
        logger.error(f"AI analysis error: {e}\n{traceback.format_exc()}")
        return json.dumps({"error": str(e), "traceback": traceback.format_exc()}, indent=2)

def sync_infrastructure(*args):
    return asyncio.run(analyze_infrastructure(*args))

def sync_ai(*args):
    return asyncio.run(analyze_ai(*args))

# Build the Gradio interface with tabs
with gr.Blocks(title="ARF v4 – Reliability Lab", theme="soft") as demo:
    gr.Markdown("# 🧠 Agentic Reliability Framework v4\n**Infrastructure & AI Reliability**")
    
    with gr.Tabs():
        with gr.TabItem("Infrastructure"):
            gr.Markdown("Enter telemetry to analyze infrastructure incidents.")
            with gr.Row():
                with gr.Column():
                    component = gr.Dropdown(
                        choices=["api-service", "auth-service", "payment-service", "database", "cache-service"],
                        value="api-service", label="Component"
                    )
                    latency = gr.Slider(10, 1000, value=100, label="Latency P99 (ms)")
                    error_rate = gr.Slider(0, 0.5, value=0.02, step=0.001, label="Error Rate")
                    throughput = gr.Number(value=1000, label="Throughput (req/s)")
                    cpu_util = gr.Slider(0, 1, value=0.4, label="CPU Utilization")
                    memory_util = gr.Slider(0, 1, value=0.3, label="Memory Utilization")
                    infra_submit = gr.Button("Analyze Infrastructure", variant="primary")
                with gr.Column():
                    infra_output = gr.JSON(label="Analysis Result")
            infra_submit.click(
                fn=sync_infrastructure,
                inputs=[component, latency, error_rate, throughput, cpu_util, memory_util],
                outputs=infra_output
            )
        
        with gr.TabItem("AI Reliability"):
            gr.Markdown("Simulate an AI query to detect hallucinations and memory drift.")
            with gr.Row():
                with gr.Column():
                    ai_component = gr.Dropdown(
                        choices=["chat", "code", "summary"], label="Task Type", value="chat"
                    )
                    prompt = gr.Textbox(label="Prompt", value="What is the capital of France?")
                    model_name = gr.Dropdown(["gpt-3.5", "gpt-4", "claude"], label="Model", value="gpt-4")
                    model_version = gr.Textbox(value="v1", label="Version")
                    confidence = gr.Slider(0, 1, value=0.95, label="Model Confidence")
                    perplexity = gr.Slider(0, 50, value=5, label="Perplexity")
                    retrieval_score = gr.Slider(0, 1, value=0.8, label="Retrieval Score")
                    ai_submit = gr.Button("Analyze AI", variant="primary")
                with gr.Column():
                    ai_output = gr.JSON(label="Analysis Result")
            ai_submit.click(
                fn=sync_ai,
                inputs=[ai_component, prompt, model_name, model_version, confidence, perplexity, retrieval_score],
                outputs=ai_output
            )
    
    gr.Markdown("""
    ---
    [📖 Tutorial](https://github.com/petter2025us/agentic-reliability-framework/blob/main/TUTORIAL.md) |
    [🐙 GitHub](https://github.com/petter2025us/agentic-reliability-framework) |
    [💼 Enterprise](mailto:petter2025us@outlook.com)
    """)

if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", server_port=7860)