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import gradio as gr
from random import randint
from all_models import models
from externalmod import gr_Interface_load
import asyncio
import os
from threading import RLock
from flask import send_file
lock = RLock()
HF_TOKEN = os.environ.get("HF_TOKEN")
def load_fn(models):
global models_load
models_load = {}
for model in models:
if model not in models_load.keys():
try:
m = gr_Interface_load(f'models/{model}', hf_token=HF_TOKEN)
except Exception as error:
print(error)
m = gr.Interface(lambda: None, ['text'], ['image'])
models_load.update({model: m})
load_fn(models)
num_models = 6
MAX_SEED = 3999999999
default_models = models[:num_models]
inference_timeout = 600
#async def infer(model_str, prompt, seed=1, timeout=inference_timeout):
# kwargs = {"seed": seed}
#task = asyncio.create_task(asyncio.to_thread(models_load[model_str].fn, prompt=prompt, **kwargs, token=HF_TOKEN))
# await asyncio.sleep(0)
# try:
# result = await asyncio.wait_for(task, timeout=timeout)
# except (Exception, asyncio.TimeoutError) as e:
# print(e)
# print(f"Task timed out: {model_str}")
# if not task.done():
# task.cancel()
# result = None
# if task.done() and result is not None:
# with lock:
# png_path = "image.png"
# result.save(png_path)
# return png_path
#return None
async def infer(model_str, prompt, seed=1, timeout=inference_timeout):
kwargs = {"seed": seed}
task = asyncio.create_task(asyncio.to_thread(models_load[model_str].fn, prompt=prompt, **kwargs, token=HF_TOKEN))
await asyncio.sleep(0)
try:
result = await asyncio.wait_for(task, timeout=timeout)
except (Exception, asyncio.TimeoutError) as e:
print(e)
print(f"Task timed out: {model_str}")
if not task.done():
task.cancel()
return None # Return None if the task fails or times out
if task.done() and result is not None:
with lock:
png_path = "image.png"
result.save(png_path) # Save the image to a file
return send_file(png_path, mimetype='image/png') # Directly use send_file here
return None # Return None if no result is generated
# Expose Gradio API
def generate_api(model_str, prompt, seed=1):
result = asyncio.run(infer(model_str, prompt, seed))
if result:
return result # Path to generated image
return None
# Launch Gradio API without frontend
iface = gr.Interface(fn=generate_api, inputs=["text", "text", "number"], outputs="file")
iface.launch(show_api=True, share=True) |