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from fastapi import FastAPI
from fastapi.staticfiles import StaticFiles
from fastapi.responses import FileResponse
import subprocess
import os
import json
import uuid
import cgi
import cgitb; cgitb.enable()
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(
[
"./truepic",
"sign",
filename,
"--assertions-inline",
json_object,
"--output",
(os.getcwd() + "/static/" + filename),
]
)
return {"response": filename}
@app.get("/verify")
def verify_image():
form = cgi.FieldStorage()
fileitem = form['fileUpload']
# check if the file has been uploaded
if fileitem.filename:
# strip the leading path from the file name
fn = os.path.basename(fileitem.filename)
# open read and write the file into the server
open(fn, 'wb').write(fileitem.file.read())
return {"response": fileitem.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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