from fastapi import APIRouter, Depends
from fastapi.responses import StreamingResponse
from PIL import Image, ImageEnhance
from fastapi import HTTPException
import io
from io import BytesIO
import requests
import os
import base64
from dotenv import load_dotenv
from pydantic import BaseModel
from pymongo import MongoClient
from models import *
from huggingface_hub import InferenceClient
from fastapi import UploadFile, File
from fastapi.responses import JSONResponse, FileResponse
import uuid
from RyuzakiLib import GeminiLatest

class FluxAI(BaseModel):
    user_id: int
    args: str
    auto_enhancer: bool = False

class MistralAI(BaseModel):
    args: str

router = APIRouter()

load_dotenv()
MONGO_URL = os.environ["MONGO_URL"]
HUGGING_TOKEN = os.environ["HUGGING_TOKEN"]
GOOGLE_API_KEY = os.environ["GOOGLE_API_KEY"]

client_mongo = MongoClient(MONGO_URL)
db = client_mongo["tiktokbot"]
collection = db["users"]

async def schellwithflux(args):
    API_URL = "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-schnell"
    headers = {"Authorization": f"Bearer {HUGGING_TOKEN}"}
    payload = {"inputs": args}
    response = requests.post(API_URL, headers=headers, json=payload)
    if response.status_code != 200:
        print(f"Error status {response.status_code}")
        return None
    return response.content

async def mistralai_post_message(message_str):
    client = InferenceClient(
        "mistralai/Mixtral-8x7B-Instruct-v0.1",
        token=HUGGING_TOKEN
    )
    output = ""
    for message in client.chat_completion(
        messages=[{"role": "user", "content": message_str}],
        max_tokens=500,
        stream=True
    ):
        output += message.choices[0].delta.content
    return output

def get_user_tokens_gpt(user_id):
    user = collection.find_one({"user_id": user_id})
    if not user:
        return 0
    return user.get("tokens", 0)

def deduct_tokens_gpt(user_id, amount):
    tokens = get_user_tokens_gpt(user_id)
    if tokens >= amount:
        collection.update_one(
            {"user_id": user_id},
            {"$inc": {"tokens": -amount}}
        )
        return True
    else:
        return False


@router.post("/akeno/mistralai", response_model=SuccessResponse, responses={422: {"model": SuccessResponse}})
async def mistralai_(payload: MistralAI):
        try:
            response = await mistralai_post_message(payload.args)
            return SuccessResponse(
                status="True",
                randydev={"message": response}
            )
        except Exception as e:
            return SuccessResponse(
                status="False",
                randydev={"error": f"An error occurred: {str(e)}"}
            )

@router.post("/akeno/fluxai", response_model=SuccessResponse, responses={422: {"model": SuccessResponse}})
async def fluxai_image(payload: FluxAI):
    if deduct_tokens_gpt(payload.user_id, amount=20):
        try:
            image_bytes = await schellwithflux(payload.args)
            if image_bytes is None:
                return SuccessResponse(
                    status="False",
                    randydev={"error": "Failed to generate an image"}
                )

            if payload.auto_enhancer:
                with Image.open(BytesIO(image_bytes)) as image:
                    enhancer = ImageEnhance.Sharpness(image)
                    image = enhancer.enhance(1.5)
                    enhancer = ImageEnhance.Contrast(image)
                    image = enhancer.enhance(1.2)
                    enhancer = ImageEnhance.Color(image)
                    image = enhancer.enhance(1.1)

                    enhanced_image_bytes = "akeno.jpg"
                    image.save(enhanced_image_bytes, format="JPEG", quality=95)
                    with open(enhanced_image_bytes, "rb") as image_file:
                        encoded_string = base64.b64encode(image_file.read())

                example_test = "Explain how this picture looks like."
                x = GeminiLatest(api_keys=GOOGLE_API_KEY)
                response = x.get_response_image(example_test, enhanced_image_bytes)
                
                return SuccessResponse(
                    status="True",
                    randydev={"image_data": encoded_string, "caption": response}
                )

            else:
                return StreamingResponse(BytesIO(image_bytes), media_type="image/jpeg")

        except Exception as e:
            return SuccessResponse(
                status="False",
                randydev={"error": f"An error occurred: {str(e)}"}
            )
    else:
        tokens = get_user_tokens_gpt(payload.user_id)
        return SuccessResponse(
            status="False",
            randydev={"error": f"Not enough tokens. Current tokens: {tokens}. Please support @xtdevs"}
        )