Update app.py

app.py

@@ -17,7 +17,7 @@ class ModelActor:
         """
         self.model = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo")
 
-    async def generate_image(self, prompt, prompt_name):
+    def generate_image(self, prompt, prompt_name):
         """
         Generates an image based on the provided prompt.
         Parameters:
@@ -29,7 +29,7 @@ class ModelActor:
         start_time = time.time()
         process_id = os.getpid()
         try:
-            output = await self.model(prompt=prompt, num_inference_steps=1, guidance_scale=0.0)
+            output = self.model(prompt=prompt, num_inference_steps=1, guidance_scale=0.0)
             if isinstance(output.images, list) and len(output.images) > 0:
                 image = output.images[0]
                 buffered = BytesIO()
@@ -57,13 +57,14 @@ async def queue_api_calls(sentence_mapping, character_dict, selected_style):
         combined_sentence = " ".join(sentences)
         prompt = generate_prompt(combined_sentence, sentence_mapping, character_dict, selected_style)
         prompts.append((paragraph_number, prompt))
-
+        print(prompts)
     num_prompts = len(prompts)
     num_actors = min(num_prompts, 20)
     model_actors = [ModelActor.remote() for _ in range(num_actors)]
     tasks = [model_actors[i % num_actors].generate_image.remote(prompt, f"Prompt {paragraph_number}") for i, (paragraph_number, prompt) in enumerate(prompts)]
 
-    responses = await asyncio.gather(*[asyncio.wrap_future(asyncio.ensure_future(ray.get(task))) for task in tasks])
+    # Use asyncio.to_thread to call ray.get() synchronously in an async context
+    responses = await asyncio.gather(*[asyncio.to_thread(ray.get, task) for task in tasks])
     images = {paragraph_number: response for (paragraph_number, _), response in zip(prompts, responses)}
     return images