import os
import time
import threading
from collections import deque
from typing import Optional, List

import google.generativeai as genai
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel

# =========================
# Config
# =========================

GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")

if not GEMINI_API_KEY:
    raise RuntimeError("GEMINI_API_KEY is not set in environment variables.")

genai.configure(api_key=GEMINI_API_KEY)

# حط الموديلات بالترتيب اللي تفضله
MODEL_POOL = [
    "gemma-3-4b-it",
    "gemma-3-12b-it",
]

LOCAL_RPM_LIMIT_PER_MODEL = 30
WINDOW_SECONDS = 60

app = FastAPI(title="Gemma Intent API", version="1.0.0")

# =========================
# Simple in-memory rate tracker
# =========================

_request_history = {model: deque() for model in MODEL_POOL}
_request_lock = threading.Lock()


def _cleanup_old_requests(model_name: str, now_ts: float) -> None:
    q = _request_history[model_name]
    while q and now_ts - q[0] > WINDOW_SECONDS:
        q.popleft()


def get_model_request_count(model_name: str) -> int:
    now_ts = time.time()
    with _request_lock:
        _cleanup_old_requests(model_name, now_ts)
        return len(_request_history[model_name])


def register_model_request(model_name: str) -> int:
    now_ts = time.time()
    with _request_lock:
        _cleanup_old_requests(model_name, now_ts)
        _request_history[model_name].append(now_ts)
        return len(_request_history[model_name])


def pick_model() -> str:
    """
    اختار أول موديل لسه تحت الحد المحلي.
    لو كلهم فوق الحد، اختار الأقل استخدامًا في آخر دقيقة.
    """
    counts = []
    for model in MODEL_POOL:
        count = get_model_request_count(model)
        counts.append((model, count))

    # أول موديل تحت الحد
    for model, count in counts:
        if count < LOCAL_RPM_LIMIT_PER_MODEL:
            return model

    # لو كلهم فوق الحد: اختار الأقل استخدامًا
    counts.sort(key=lambda x: x[1])
    return counts[0][0]


def get_fallback_models(primary_model: str) -> List[str]:
    return [m for m in MODEL_POOL if m != primary_model]


# =========================
# Request / Response Models
# =========================

class ChatRequest(BaseModel):
    message: str
    system_prompt: Optional[str] = (
        "You are an intent classification assistant. "
        "Return a short direct answer only."
    )
    temperature: Optional[float] = 0.1
    max_output_tokens: Optional[int] = 80


class ChatResponse(BaseModel):
    success: bool
    model_used: str
    input_message: str
    reply: str
    requests_last_minute_for_model: int
    total_requests_last_minute_all_models: int


# =========================
# Helpers
# =========================

def total_requests_last_minute() -> int:
    return sum(get_model_request_count(model) for model in MODEL_POOL)


def build_prompt(system_prompt: str, user_message: str) -> str:
    return f"{system_prompt}\n\nUser: {user_message}\nAssistant:"


def is_rate_limit_error(exc: Exception) -> bool:
    msg = str(exc).lower()
    rate_limit_markers = [
        "429",
        "quota",
        "rate limit",
        "resource exhausted",
        "too many requests",
    ]
    return any(marker in msg for marker in rate_limit_markers)


def generate_with_model(
    model_name: str,
    prompt: str,
    temperature: float,
    max_output_tokens: int
) -> str:
    generation_config = genai.types.GenerationConfig(
        temperature=temperature,
        max_output_tokens=max_output_tokens,
        top_p=0.95,
    )

    model = genai.GenerativeModel(model_name)
    response = model.generate_content(
        prompt,
        generation_config=generation_config
    )

    try:
        return response.text.strip()
    except Exception:
        return "Model returned an empty response."


def generate_reply_with_fallback(
    user_message: str,
    system_prompt: str,
    temperature: float,
    max_output_tokens: int
):
    prompt = build_prompt(system_prompt, user_message)

    primary_model = pick_model()
    candidate_models = [primary_model] + get_fallback_models(primary_model)

    last_error = None

    for model_name in candidate_models:
        local_count_before = get_model_request_count(model_name)

        print(f"[INFO] Trying model: {model_name}")
        print(f"[INFO] Local requests in last minute for {model_name}: {local_count_before}")

        try:
            reply = generate_with_model(
                model_name=model_name,
                prompt=prompt,
                temperature=temperature,
                max_output_tokens=max_output_tokens,
            )

            used_count = register_model_request(model_name)
            return reply, model_name, used_count

        except Exception as e:
            last_error = e
            print(f"[WARN] Model failed: {model_name}")
            print(f"[WARN] Error: {str(e)}")

            # لو Rate Limit جرّب اللي بعده
            if is_rate_limit_error(e):
                continue

            # لو خطأ عادي برضه جرّب اللي بعده
            continue

    raise Exception(f"All models failed. Last error: {last_error}")


# =========================
# Routes
# =========================

@app.get("/")
def home():
    return {
        "status": "ok",
        "message": "Gemma Intent API is running",
        "models": MODEL_POOL,
        "local_rpm_limit_per_model": LOCAL_RPM_LIMIT_PER_MODEL
    }


@app.get("/stats")
def stats():
    return {
        "per_model_requests_last_minute": {
            model: get_model_request_count(model)
            for model in MODEL_POOL
        },
        "total_requests_last_minute": total_requests_last_minute()
    }


@app.post("/chat", response_model=ChatResponse)
def chat(req: ChatRequest):
    if not req.message or not req.message.strip():
        raise HTTPException(status_code=400, detail="message is required")

    print("\n========== NEW REQUEST ==========")
    print("Incoming message:")
    print(req.message)
    print(f"Total requests last minute (all models): {total_requests_last_minute()}")

    try:
        reply, model_used, used_count = generate_reply_with_fallback(
            user_message=req.message,
            system_prompt=req.system_prompt or "You are a helpful assistant.",
            temperature=req.temperature if req.temperature is not None else 0.1,
            max_output_tokens=req.max_output_tokens if req.max_output_tokens is not None else 80,
        )

        print(f"Model used: {model_used}")
        print(f"Requests last minute for model after call: {used_count}")
        print("Model reply:")
        print(reply)
        print("=================================\n")

        return ChatResponse(
            success=True,
            model_used=model_used,
            input_message=req.message,
            reply=reply,
            requests_last_minute_for_model=used_count,
            total_requests_last_minute_all_models=total_requests_last_minute()
        )

    except Exception as e:
        print("\nERROR:")
        print(str(e))
        print("=================================\n")
        raise HTTPException(status_code=500, detail=str(e))