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from flask import Flask, request, jsonify, send_file
from flask_cors import CORS
import asyncio
import tempfile
import os
from threading import RLock
from huggingface_hub import InferenceClient
from all_models import models # Importing models from all_models
myapp = Flask(__name__)
CORS(myapp) # Enable CORS for all routes
lock = RLock()
HF_TOKEN = os.environ.get("HF_TOKEN") # Hugging Face token
inference_timeout = 600 # Set timeout for inference
@myapp.route('/')
def home():
return "Welcome to the Image Background Remover!"
# Function to dynamically load models from the "models" list
def get_model_from_name(model_name):
return model_name if model_name in models else None
# Asynchronous function to perform inference
async def infer(client, prompt, seed=1, timeout=inference_timeout, model="prompthero/openjourney-v4"):
task = asyncio.create_task(
asyncio.to_thread(client.text_to_image, prompt=prompt, seed=seed, model=model)
)
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 for model: {model}")
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)
with open(temp_image.name, "wb") as f:
f.write(result) # Save the result image as a temporary file
return temp_image.name # Return the path to the saved image
return None
# Flask route for the API endpoint
@myapp.route('/generate_api', methods=['POST'])
def generate_api():
data = request.get_json()
# Extract required fields from the request
prompt = data.get('prompt', '')
seed = data.get('seed', 1)
model_name = data.get('model', 'prompthero/openjourney-v4') # Default to "prompthero/openjourney-v4" if not provided
if not prompt:
return jsonify({"error": "Prompt is required"}), 400
# Get the model from all_models
model = get_model_from_name(model_name)
if not model:
return jsonify({"error": f"Model '{model_name}' not found in available models"}), 400
try:
# Create a generic InferenceClient for the model
client = InferenceClient(token=HF_TOKEN) # Pass Hugging Face token if needed
# Call the async inference function
result_path = asyncio.run(infer(client, prompt, seed, model=model))
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
# Add this block to make sure your app runs when called
if __name__ == "__main__":
myapp.run(host='0.0.0.0', port=7860) # Run directly if needed for testing |