Update app.py

app.py

@@ -1,77 +1,11 @@
-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
-
-app = Flask(__name__)
-CORS(app)  # Enable CORS for all routes
-
-lock = RLock()
-HF_TOKEN = os.environ.get("HF_TOKEN")  # Hugging Face token
-
-inference_timeout = 600  # Set timeout for inference
-
-# 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
-@app.route('/generate_api', methods=['POST'])
-def generate_api():
-    data = request.get_json()
+# app.py
 
-    # 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
+import os
+import subprocess
 
 if __name__ == "__main__":
-    app.run(host='0.0.0.0', port=7860)  # Run directly if needed for testing
+    # Run awake.py in the background
+    subprocess.Popen(["python", "wk.py"])  # Start awake.py
+
+    # Run the Flask app using Gunicorn
+    os.system("gunicorn -w 4 -b 0.0.0.0:7860 myapp:myapp")  # 4 worker processes