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from flask import Flask, request, jsonify, send_file
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 PIL import Image
app = Flask(__name__)
lock = RLock()
HF_TOKEN = os.environ.get("HF_TOKEN")
# Load models
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
# Gradio inference function
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 = "generated_image.png"
result.save(png_path) # Save the result as an image
return png_path
return None
# API function to perform inference
@app.route('/generate-image', methods=['POST'])
def generate_image():
data = request.get_json()
model_str = data['model_str']
prompt = data['prompt']
seed = data.get('seed', 1)
# Run Gradio inference
result_path = asyncio.run(infer(model_str, prompt, seed))
if result_path:
# Send back the generated image file
return send_file(result_path, mimetype='image/png')
else:
return jsonify({"error": "Failed to generate image."}), 500
# Add this block to make sure your app runs when called
if __name__ == "__main__":
app.run(host='0.0.0.0', port=7860) # Run directly |