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# https://medium.com/@qacheampong/building-and-deploying-a-fastapi-app-with-hugging-face-9210e9b4a713
# https://huggingface.co/spaces/Queensly/FastAPI_in_Docker
from fastapi import FastAPI,Request
import uvicorn
import json
from PIL import Image
import time
from constants import DESCRIPTION, LOGO
from model import get_pipeline
from utils import replace_background
from diffusers.utils import load_image
import base64
app = FastAPI()
pipeline = get_pipeline()
#Endpoints
#Root endpoints
@app.get("/")
def root():
return {"API": "Sum of 2 Squares"}
@app.post("/img2img")
async def predict(url:str,prompt:str):
MAX_QUEUE_SIZE = 4
start = time.time()
url = "https://img2.baidu.com/it/u=1845675188,2679793929&fm=253&fmt=auto&app=138&f=JPEG?w=667&h=500"
prompt = "a nice Comfortable and clean. According to Baidu Education Information, the adjectives for a room include: comfortable, clean, beautiful, spacious, warm, quiet, luxurious, pleasant, exquisite, and warm ,colorful, light room width sofa,8k"
init_image = load_image(url).convert("RGB")
# image1 = replace_background(init_image.resize((256, 256)))
w, h = init_image.size
newW = 512
newH = int(h * newW / w)
img = init_image.resize((newW, newH))
end1 = time.time()
print("加载管道:", end1 - start)
result = pipeline(
prompt=prompt,
image=img,
strength=0.6,
seed=10,
width=256,
height=256,
guidance_scale=1,
num_inference_steps=4,
)
output_image = result.images[0]
end2 = time.time()
print("测试",output_image)
print("s生成完成:", end2 - end1)
output_image.save("./imageclm5.png")
# 将图片对象转换为bytes
image_bytes = output_image.to_bytes()
# 对bytes进行base64编码
encoded_string = base64.b64encode(image_bytes).decode('utf-8')
return encoded_string
@app.post("/predict")
async def predict(request:Request):
body = await request.body()
data = json.loads(body)
prompt = data.get("prompt")
return f"您好,{prompt}"
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