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from fastapi import FastAPI, UploadFile
from fastapi.staticfiles import StaticFiles
from fastapi.responses import FileResponse
import subprocess
import os
import json
import uuid
import logging
import torch
from diffusers import (
StableDiffusionPipeline,
DPMSolverMultistepScheduler,
EulerDiscreteScheduler,
)
app = FastAPI()
@app.get("/generate")
def generate_image(prompt, model):
torch.cuda.empty_cache()
modelArray = model.split(",")
modelName = modelArray[0]
modelVersion = modelArray[1]
pipeline = StableDiffusionPipeline.from_pretrained(
str(modelName), torch_dtype=torch.float16
)
pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config)
pipeline = pipeline.to("cuda")
image = pipeline(prompt, num_inference_steps=50, height=512, width=512).images[0]
filename = str(uuid.uuid4()) + ".jpg"
image.save(filename)
# assertion = {
# "assertions": [
# {
# "label": "com.truepic.custom.ai",
# "data": {
# "model_name": modelName,
# "model_version": modelVersion,
# "prompt": prompt,
# },
# }
# ]
# }
# json_object = json.dumps(assertion)
subprocess.check_output(
[
"./scripts/sign.sh",
filename,
]
)
return {"response": filename}
@app.post("/verify")
def verify_image(fileUpload: UploadFile):
logging.warning("in verify")
logging.warning(fileUpload.filename)
# check if the file has been uploaded
if fileUpload.filename:
# strip the leading path from the file name
fn = os.path.basename(fileUpload.filename)
# open read and write the file into the server
open(fn, "wb").write(fileUpload.file.read())
response = subprocess.check_output(
[
"./scripts/verify.sh",
fileUpload.filename,
]
)
logging.warning(response)
return {"response": fileUpload.filename}
app.mount("/", StaticFiles(directory="static", html=True), name="static")
@app.get("/")
def index() -> FileResponse:
return FileResponse(path="/app/static/index.html", media_type="text/html")
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