import os
from transformers import safetensors_conversion
safetensors_conversion.auto_conversion = lambda *args, **kwargs: None

os.environ["TRANSFORMERS_NO_ADVISORY_WARNINGS"] = "1"

import uuid
import base64
from huggingface_hub import login, get_token
from fastapi import FastAPI, UploadFile, File, Header, HTTPException
from fastapi.responses import StreamingResponse
from pydantic import BaseModel
from typing import Optional

# Authenticate
hf_token = os.environ.get("HF_TOKEN") or get_token()
if hf_token:
    login(token=hf_token)
else:
    raise RuntimeError(
        "No HuggingFace token found. "
        "Set HF_TOKEN environment variable or run `huggingface-cli login`."
    )

from memory import get_history, append_turn, clear_session
from stt_module.pipeline import stt_pipeline
from text_module.router import prepare_user_message
from llm.engine import llm_engine

app = FastAPI(title="FarmLingua AI", version="1.0.0")


# Helpers

def resolve_uid(x_uid: Optional[str]) -> str:
    return x_uid if x_uid else str(uuid.uuid4())


def encode_header(value: str) -> str:
    """Base64-encode header values that may contain non-latin-1 characters."""
    return base64.b64encode(value.encode("utf-8")).decode("ascii")


def stream_llm(uid: str, channel: str, user_message: str):
    history = get_history(uid, channel)
    append_turn(uid, channel, "user", user_message)

    streamer      = llm_engine.stream(history, user_message)
    full_response = []

    for token in streamer:
        full_response.append(token)
        yield token

    assistant_reply = "".join(full_response).strip()
    append_turn(uid, channel, "assistant", assistant_reply)



# Routes 

@app.post("/stt/transcribe-and-chat")
async def stt_transcribe_and_chat(
    audio: UploadFile = File(...),
    x_uid: Optional[str] = Header(default=None),
):
    uid         = resolve_uid(x_uid)
    audio_bytes = await audio.read()

    try:
        stt_result = stt_pipeline.transcribe(audio_bytes)
    except ValueError as e:
        raise HTTPException(status_code=422, detail=str(e))

    transcription = stt_result["transcription"]

    headers = {
        "X-UID":           uid,
        "X-Transcription": encode_header(transcription),   # base64 — safe for latin-1
        "X-Language":      stt_result["language"],
        "X-Confidence":    str(stt_result["confidence"]),
        "Access-Control-Expose-Headers": "X-UID, X-Transcription, X-Language, X-Confidence",
    }

    try:
        return StreamingResponse(
            stream_llm(uid, "stt", transcription),
            media_type="text/plain",
            headers=headers,
        )
    except RuntimeError as e:
        raise HTTPException(status_code=503, detail=str(e))


class TextRequest(BaseModel):
    message: str


@app.post("/text/chat")
async def text_chat(
    body: TextRequest,
    x_uid: Optional[str] = Header(default=None),
):
    uid = resolve_uid(x_uid)

    try:
        user_message = prepare_user_message(body.message)
    except ValueError as e:
        raise HTTPException(status_code=400, detail=str(e))

    headers = {
        "X-UID": uid,
        "Access-Control-Expose-Headers": "X-UID",
    }

    try:
        return StreamingResponse(
            stream_llm(uid, "text", user_message),
            media_type="text/plain",
            headers=headers,
        )
    except RuntimeError as e:
        raise HTTPException(status_code=503, detail=str(e))


@app.delete("/session")
async def clear_user_session(x_uid: str = Header(...)):
    clear_session(x_uid)
    return {"status": "cleared", "uid": x_uid}


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