Update app.py

app.py

@@ -6,6 +6,7 @@ from diffusers import AutoPipelineForText2Image
 from io import BytesIO
 import gradio as gr
 import ray
+from ray.util import ActorPool
 
 ray.init()
 
@@ -43,10 +44,10 @@ class ModelActor:
             print(f"[{process_id}] Error generating image for {prompt_name}: {e}")
             return None
 
-# Create multiple instances of ModelActor
-model_actors = [ModelActor.remote() for _ in range(3)]
+def create_actor_pool(num_actors):
+    return ActorPool([ModelActor.remote() for _ in range(num_actors)])
 
-async def queue_api_calls(sentence_mapping, character_dict, selected_style):
+async def queue_api_calls(sentence_mapping, character_dict, selected_style, pool):
     print(f"queue_api_calls invoked with sentence_mapping: {sentence_mapping}, character_dict: {character_dict}, selected_style: {selected_style}")
     prompts = []
 
@@ -57,7 +58,7 @@ async def queue_api_calls(sentence_mapping, character_dict, selected_style):
         prompts.append((paragraph_number, prompt))
         print(f"Generated prompt for paragraph {paragraph_number}: {prompt}")
 
-    tasks = [model_actors[i % len(model_actors)].generate_image.remote(prompt, f"Prompt {paragraph_number}") for i, (paragraph_number, prompt) in enumerate(prompts)]
+    tasks = [pool.submit(lambda actor, prompt, prompt_name: actor.generate_image.remote(prompt, prompt_name), prompt, f"Prompt {paragraph_number}") for paragraph_number, prompt in prompts]
     print("Tasks created for image generation.")
 
     responses = await asyncio.gather(*[asyncio.to_thread(ray.get, task) for task in tasks])
@@ -76,7 +77,8 @@ def process_prompt(sentence_mapping, character_dict, selected_style):
         asyncio.set_event_loop(loop)
     print("Event loop created.")
 
-    cmpt_return = loop.run_until_complete(queue_api_calls(sentence_mapping, character_dict, selected_style))
+    pool = create_actor_pool(min(20, max(1, len(sentence_mapping))))  # Create pool with dynamic size
+    cmpt_return = loop.run_until_complete(queue_api_calls(sentence_mapping, character_dict, selected_style, pool))
     print(f"process_prompt completed with return value: {cmpt_return}")
     return cmpt_return