import os
import json
from typing import Optional
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel, Field
from huggingface_hub import InferenceClient
import uvicorn
import urllib.request
import urllib.error


app = FastAPI()

# Token da Settings → Secrets (obbligatorio per Inference API / Inference Endpoints)
HF_TOKEN = os.environ.get("HF_TOKEN")
# Modello: default Ministral-3-14B-Instruct (multilingue, system prompt, Apache 2.0)
# Se usi Inference Endpoints, imposta HF_INFERENCE_ENDPOINT_URL con l'URL dell'endpoint
MODEL_ID = os.environ.get("MODEL_ID", "mistralai/Ministral-3-14B-Instruct-2512")
INFERENCE_ENDPOINT_URL = os.environ.get("HF_INFERENCE_ENDPOINT_URL")

# Se hai un Inference Endpoint dedicato (es. per Ministral-3-14B), usalo; altrimenti provider auto/hf-inference
if INFERENCE_ENDPOINT_URL:
    client = InferenceClient(model=INFERENCE_ENDPOINT_URL, token=HF_TOKEN)
    client_hf = client
else:
    client = InferenceClient(model=MODEL_ID, token=HF_TOKEN)
    client_hf = InferenceClient(model=MODEL_ID, token=HF_TOKEN, provider="hf-inference")


def build_messages(system_prompt: str, history: list, prompt: str) -> list:
    """Costruisce la lista messages per chat_completion (task conversational)."""
    messages = []
    if system_prompt:
        messages.append({"role": "system", "content": system_prompt})
    for pair in history or []:
        user_msg = pair[0] if isinstance(pair, (list, tuple)) else pair.get("user", "")
        bot_msg = pair[1] if isinstance(pair, (list, tuple)) else pair.get("bot", "")
        messages.append({"role": "user", "content": user_msg})
        messages.append({"role": "assistant", "content": bot_msg})
    messages.append({"role": "user", "content": prompt})
    return messages


def format_prompt_for_text_generation(system_prompt: str, history: list, prompt: str) -> str:
    """Formato Mistral [INST] per text_generation quando il provider non supporta chat."""
    full_message = f"{system_prompt}, {prompt}" if system_prompt else prompt
    out = "<s>"
    for pair in history or []:
        user_msg = pair[0] if isinstance(pair, (list, tuple)) else pair.get("user", "")
        bot_msg = pair[1] if isinstance(pair, (list, tuple)) else pair.get("bot", "")
        out += f"[INST] {user_msg} [/INST] {bot_msg}</s> "
    out += f"[INST] {full_message} [/INST]"
    return out


def chat_completion_via_http(messages: list, max_tokens: int, temperature: float, top_p: float) -> Optional[str]:
    """
    Chiamata diretta all'endpoint HF chat completions (v1).
    Usata quando il SDK fallisce perché il modello non dichiara il task (es. Ministral-3).
    """
    if not HF_TOKEN:
        return None
    base = INFERENCE_ENDPOINT_URL.rstrip("/") if INFERENCE_ENDPOINT_URL else f"https://api-inference.huggingface.co/models/{MODEL_ID}"
    url = f"{base}/v1/chat/completions"
    body = {
        "messages": messages,
        "max_tokens": max_tokens,
        "temperature": temperature,
        "top_p": top_p,
    }
    data = json.dumps(body).encode("utf-8")
    req = urllib.request.Request(
        url,
        data=data,
        headers={
            "Authorization": f"Bearer {HF_TOKEN}",
            "Content-Type": "application/json",
        },
        method="POST",
    )
    try:
        with urllib.request.urlopen(req, timeout=120) as resp:
            out = json.loads(resp.read().decode())
    except urllib.error.HTTPError as e:
        if e.code == 503:
            err_body = e.read().decode() if e.fp else ""
            try:
                err_json = json.loads(err_body)
                if "estimated_time" in err_json:
                    raise HTTPException(
                        status_code=503,
                        detail=f"Modello in caricamento. Riprova tra {err_json.get('estimated_time', 0):.0f}s.",
                    )
            except (ValueError, TypeError):
                pass
        return None
    except Exception:
        return None
    choices = out.get("choices") or []
    if not choices:
        return None
    msg = choices[0].get("message") or {}
    return (msg.get("content") or "").strip()


class Item(BaseModel):
    """Payload per /generate/. Per Ministral-3: temperature < 0.1 in produzione (raccomandato)."""
    prompt: str
    history: list = Field(default_factory=list, description="Lista di coppie (user_msg, bot_msg)")
    system_prompt: str = ""
    temperature: float = Field(default=0.0, description="Ministral-3: < 0.1 per produzione")
    max_new_tokens: int = 1048
    top_p: float = 0.15
    repetition_penalty: float = 1.0


def generate(item: Item) -> str:
    if not HF_TOKEN:
        raise HTTPException(
            status_code=500,
            detail="HF_TOKEN non configurato. Imposta il secret HF_TOKEN in Settings → Secrets dello Space.",
        )
    temperature = float(item.temperature)
    if temperature < 1e-2:
        temperature = 1e-2
    top_p = float(item.top_p)

    messages = build_messages(item.system_prompt, item.history or [], item.prompt)
    last_error = None

    # 1) Prova chat_completion con provider di default (es. Together)
    try:
        response = client.chat_completion(
            messages=messages,
            max_tokens=item.max_new_tokens,
            temperature=temperature,
            top_p=top_p,
        )
        if response.choices and len(response.choices) > 0:
            return response.choices[0].message.content or ""
        return ""
    except Exception as e1:
        last_error = e1

    # 2) Riprova chat con hf-inference (supporta modelli come Mixtral-8x7B che su altri provider non sono "chat")
    try:
        response = client_hf.chat_completion(
            messages=messages,
            max_tokens=item.max_new_tokens,
            temperature=temperature,
            top_p=top_p,
        )
        if response.choices and len(response.choices) > 0:
            return response.choices[0].message.content or ""
        return ""
    except Exception as e2:
        last_error = e2

    # 3) Chat completions via HTTP (endpoint v1) – funziona per modelli che non dichiarano il task (es. Ministral-3)
    try:
        content = chat_completion_via_http(
            messages, item.max_new_tokens, temperature, top_p
        )
        if content is not None and content != "":
            return content
    except HTTPException:
        raise
    except Exception as e3:
        last_error = e3

    # 4) Ultima risorsa: text_generation (solo per modelli che lo supportano su hf-inference)
    try:
        formatted = format_prompt_for_text_generation(
            item.system_prompt, item.history or [], item.prompt
        )
        stream = client_hf.text_generation(
            formatted,
            max_new_tokens=item.max_new_tokens,
            temperature=temperature,
            top_p=top_p,
            do_sample=True,
            return_full_text=False,
        )
        if isinstance(stream, str):
            return stream
        if hasattr(stream, "__iter__") and not isinstance(stream, str):
            return "".join(
                r.token.text if hasattr(r, "token") and hasattr(r.token, "text") else (r if isinstance(r, str) else "")
                for r in stream
            )
        return str(stream)
    except Exception as e4:
        last_error = e4

    raise HTTPException(status_code=502, detail=f"Inference fallita: {str(last_error)}")


@app.get("/")
def root():
    return {"status": "ok", "model": MODEL_ID}


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


@app.post("/generate/")
async def generate_text(item: Item):
    return {"response": generate(item)}