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import gradio as gr
from random import randint
from all_models import models # Import the list of available models
from externalmod import gr_Interface_load
import asyncio
import os
from threading import RLock
from flask import Flask, request, jsonify, send_file
from flask_cors import CORS
import tempfile
lock = RLock()
HF_TOKEN = os.environ.get("HF_TOKEN")
app = Flask(__name__)
CORS(app) # Enable CORS for all routes
# Function to 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 # Number of models to load initially
MAX_SEED = 3999999999
default_models = models[:num_models] # Load the first few models for inference
inference_timeout = 600
# Asynchronous function to perform inference
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:
temp_image = tempfile.NamedTemporaryFile(suffix=".png", delete=False)
result.save(temp_image.name) # Save result as a temporary file
return temp_image.name # Return the path to the saved image
return None
# Flask route for the API endpoint
@app.route('/generate_api', methods=['POST'])
def generate_api():
data = request.get_json()
# Extract required fields from the request
model_str = data.get('model_str', default_models[0]) # Default to first model if not provided
prompt = data.get('prompt', '')
seed = data.get('seed', 1)
if not prompt:
return jsonify({"error": "Prompt is required"}), 400
try:
# Call the async inference function
result_path = asyncio.run(infer(model_str, prompt, seed))
if result_path:
return send_file(result_path, mimetype='image/png') # Send back the generated image file
else:
return jsonify({"error": "Failed to generate image"}), 500
except Exception as e:
return jsonify({"error": str(e)}), 500
if __name__ == '__main__':
app.run(debug=True) |