Update app.py

app.py

@@ -17,21 +17,19 @@ 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:
             - prompt (str): The input text for image generation.
             - prompt_name (str): A name for the prompt, used for logging.
-
         Returns:
             bytes: The generated image data in bytes format, or None if generation fails.
         """
         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()
@@ -47,12 +45,10 @@ class ModelActor:
 async def queue_api_calls(sentence_mapping, character_dict, selected_style):
     """
     Generates images for all provided prompts in parallel using Ray actors.
-
     Parameters:
         - sentence_mapping (dict): Mapping between paragraph numbers and sentences.
         - character_dict (dict): Dictionary mapping characters to their descriptions.
         - selected_style (str): Selected illustration style.
-
     Returns:
         dict: A dictionary where keys are paragraph numbers and values are image data in bytes format.
     """
@@ -67,19 +63,17 @@ async def queue_api_calls(sentence_mapping, character_dict, selected_style):
     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.to_thread(ray.get, task) for task in tasks])
+    responses = await asyncio.gather(*[ray.get(task) for task in tasks])
     images = {paragraph_number: response for (paragraph_number, _), response in zip(prompts, responses)}
     return images
 
 def process_prompt(sentence_mapping, character_dict, selected_style):
     """
     Processes the provided prompts and generates images.
-
     Parameters:
         - sentence_mapping (dict): Mapping between paragraph numbers and sentences.
         - character_dict (dict): Dictionary mapping characters to their descriptions.
         - selected_style (str): Selected illustration style.
-
     Returns:
         dict: A dictionary where keys are paragraph numbers and values are image data in bytes format.
     """
@@ -99,4 +93,4 @@ gradio_interface = gr.Interface(
 )
 
 if __name__ == "__main__":
-    gradio_interface.launch()
+    gradio_interface.launch()