from multiprocessing import context
from httplib2 import Response
import torch
import uuid
import os
from diffusers import StableDiffusionImg2ImgPipeline
from dotenv import load_dotenv
from os import getenv
from fastapi import FastAPI, Response, HTTPException, File, UploadFile
from pydantic import BaseModel
import io
from PIL.PngImagePlugin import PngInfo
from PIL import Image

load_dotenv()

pipe = StableDiffusionImg2ImgPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", num_inference_steps=100, revision="fp16", torch_dtype=torch.float16, use_auth_token=getenv("TOKEN"))
pipe.to("cuda")

class Text(BaseModel):
    text: str



app = FastAPI()

@app.post("/",
    responses = {
        200: {
            "content": {"image/png": {}}
        }
    },
    response_class=Response
)
async def root(text: str, my_file: UploadFile = File(...)):
    prompt = text.replace('+', ' ')
    print(prompt)
    request_object_content = await my_file.read()
    img = Image.open(io.BytesIO(request_object_content))

    height_orig = img.height
    width_orig = img.width
    aspect_ratio = width_orig / height_orig 
    width_new = 512
    height_new = int(width_new / aspect_ratio)
    img = img.resize((width_new, height_new), 0)
    try:  
        resp = pipe(prompt, image=img)
        print(resp)
        image = resp.images[0]
    except RuntimeError as e:
        print(e)
        raise HTTPException(status_code=202, detail="Busy")
    except Exception as e:
        raise HTTPException(status_code=504, detail=str(e)) 
    if resp["nsfw_content_detected"] == [True]:
        raise HTTPException(status_code=418, detail="NSFW")
    
    imgByteArr = io.BytesIO()
    image.save(imgByteArr, format="PNG")
    imgByteArr = imgByteArr.getvalue()
    running = False
    return Response(content=imgByteArr, media_type="image/png")