import os
import json
import random
import datetime
import numpy as np
import gradio as gr
import requests
import faiss
from fastapi import FastAPI, Body, Header, HTTPException
from pydantic import BaseModel
from sentence_transformers import SentenceTransformer
from filelock import FileLock

# === Config ===
HF_TOKEN = os.getenv("HF_TOKEN", "").strip()
API_KEY = os.getenv("API_KEY", "").strip()

HF_API_URL = "https://router.huggingface.co/hf-inference/v1/completions"
HEADERS = {"Authorization": f"Bearer {HF_TOKEN}"} if HF_TOKEN else {}

# === FAISS Setup ===
VECTOR_DIM = 384
INDEX_FILE = "incident_vectors.index"
TEXTS_FILE = "incident_texts.json"
LOCK_FILE = "faiss_save.lock"

model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")

if os.path.exists(INDEX_FILE):
    index = faiss.read_index(INDEX_FILE)
    with open(TEXTS_FILE, "r") as f:
        incident_texts = json.load(f)
else:
    index = faiss.IndexFlatL2(VECTOR_DIM)
    incident_texts = []


# === Safe persistence ===
def save_index():
    with FileLock(LOCK_FILE):
        faiss.write_index(index, INDEX_FILE)
        with open(TEXTS_FILE, "w") as f:
            json.dump(incident_texts, f)


# === Core logic ===
events = []


def detect_anomaly(event):
    """Adaptive threshold-based anomaly detection."""
    latency = event["latency"]
    error_rate = event["error_rate"]

    # Occasionally flag random anomaly for testing
    if random.random() < 0.25:
        return True

    return latency > 150 or error_rate > 0.05


def call_huggingface_analysis(prompt):
    """Uses HF Inference API or local fallback."""
    if not HF_TOKEN:
        return "Offline mode: simulated analysis."

    try:
        payload = {
            "model": "mistralai/Mixtral-8x7B-Instruct-v0.1",
            "prompt": prompt,
            "max_tokens": 200,
            "temperature": 0.3,
        }
        response = requests.post(HF_API_URL, headers=HEADERS, json=payload, timeout=10)
        if response.status_code == 200:
            result = response.json()
            return result.get("choices", [{}])[0].get("text", "").strip()
        else:
            return f"Error {response.status_code}: {response.text}"
    except Exception as e:
        return f"Error generating analysis: {e}"


def simulate_healing(event):
    actions = [
        "Restarted container",
        "Scaled up instance",
        "Cleared queue backlog",
        "No actionable step detected.",
    ]
    return random.choice(actions)


def analyze_event(component, latency, error_rate):
    event = {
        "timestamp": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
        "component": component,
        "latency": latency,
        "error_rate": error_rate,
    }

    is_anomaly = detect_anomaly(event)
    event["anomaly"] = is_anomaly
    event["status"] = "Anomaly" if is_anomaly else "Normal"

    prompt = (
        f"Component: {component}\nLatency: {latency:.2f}ms\nError Rate: {error_rate:.3f}\n"
        f"Status: {event['status']}\n\n"
        "Provide a one-line reliability insight or root cause analysis."
    )

    # AI Reliability analysis
    analysis = call_huggingface_analysis(prompt)
    event["analysis"] = analysis

    # Simulated self-healing
    healing_action = simulate_healing(event)
    event["healing_action"] = healing_action

    # === Vector learning & persistence ===
    vector_text = f"{component} {latency} {error_rate} {analysis}"
    vec = model.encode([vector_text])
    index.add(np.array(vec, dtype=np.float32))
    incident_texts.append(vector_text)
    save_index()

    # Similar incident lookup
    if len(incident_texts) > 1:
        D, I = index.search(vec, k=min(3, len(incident_texts)))
        similar = [incident_texts[i] for i in I[0] if i < len(incident_texts)]
        if similar:
            event["healing_action"] += f" Found {len(similar)} similar incidents (e.g., {similar[0][:100]}...)."
    else:
        event["healing_action"] += " - Not enough incidents stored yet."

    events.append(event)
    return event


# === FastAPI backend ===
app = FastAPI(title="Agentic Reliability Framework API")


class AddEventModel(BaseModel):
    component: str
    latency: float
    error_rate: float


def verify_api_key(provided_key: str):
    if not API_KEY:
        return True  # dev mode
    return provided_key == API_KEY


@app.post("/add-event")
def add_event(
    payload: AddEventModel = Body(...),
    x_api_key: str = Header(None, alias="X-API-Key"),
):
    """Add a telemetry event (secured via API key)."""
    if not verify_api_key(x_api_key):
        raise HTTPException(status_code=401, detail="Unauthorized: invalid API key.")

    try:
        event = analyze_event(payload.component, payload.latency, payload.error_rate)
        return {"status": "ok", "event": event}
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Failed to add event: {e}")


# === Gradio Dashboard ===
def submit_event(component, latency, error_rate):
    event = analyze_event(component, latency, error_rate)

    table = [
        [
            e["timestamp"],
            e["component"],
            e["latency"],
            e["error_rate"],
            e["status"],
            e["analysis"],
            e["healing_action"],
        ]
        for e in events[-20:]
    ]

    return (
        f"✅ Event Processed ({event['status']})",
        gr.Dataframe(
            headers=[
                "timestamp",
                "component",
                "latency",
                "error_rate",
                "status",
                "analysis",
                "healing_action",
            ],
            value=table,
        ),
    )


with gr.Blocks(title="🧠 Agentic Reliability Framework MVP") as demo:
    gr.Markdown(
        "## 🧠 Agentic Reliability Framework MVP\n"
        "Adaptive anomaly detection + AI-driven self-healing + persistent FAISS memory"
    )
    with gr.Row():
        component = gr.Textbox(label="Component", value="api-service")
        latency = gr.Slider(10, 400, value=100, step=1, label="Latency (ms)")
        error_rate = gr.Slider(0, 0.2, value=0.02, step=0.001, label="Error Rate")
    submit = gr.Button("🚀 Submit Telemetry Event")
    output_text = gr.Textbox(label="Detection Output")
    table_output = gr.Dataframe(
        headers=[
            "timestamp",
            "component",
            "latency",
            "error_rate",
            "status",
            "analysis",
            "healing_action",
        ]
    )
    submit.click(fn=submit_event, inputs=[component, latency, error_rate], outputs=[output_text, table_output])


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