Update app.py

app.py

@@ -8,10 +8,10 @@ import gradio as gr
 # Load the model once outside of the function
 model = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo")
 
-async def generate_image(prompt, prompt_name):
+def generate_image(prompt, prompt_name):
     try:
         print(f"Generating response for {prompt_name}")
-        output = await model(prompt=prompt, num_inference_steps=1, guidance_scale=0.0)
+        output = model(prompt=prompt, num_inference_steps=1, guidance_scale=0.0)
 
         # Check if the model returned images
         if isinstance(output.images, list) and len(output.images) > 0:
@@ -39,12 +39,13 @@ async def queue_api_calls(sentence_mapping, character_dict, selected_style):
     for paragraph_number, sentences in sentence_mapping.items():
         combined_sentence = " ".join(sentences)
         print(f'combined_sentence: {combined_sentence}, character_dict: {character_dict}, selected_style: {selected_style}')
-        prompt = generate_prompt(combined_sentence, sentence_mapping, character_dict, selected_style)
+        prompt = generate_prompt(combined_sentence, character_dict, selected_style)
         prompts.append((paragraph_number, prompt))
         print(f"Generated prompt for paragraph {paragraph_number}: {prompt}")
 
     # Generate images for each prompt in parallel
-    tasks = [generate_image(prompt, f"Prompt {paragraph_number}") for paragraph_number, prompt in prompts]
+    loop = asyncio.get_running_loop()
+    tasks = [loop.run_in_executor(None, generate_image, prompt, f"Prompt {paragraph_number}") for paragraph_number, prompt in prompts]
     responses = await asyncio.gather(*tasks)
     
     images = {paragraph_number: response for (paragraph_number, _), response in zip(prompts, responses)}