Update app.py

app.py

@@ -4,7 +4,7 @@ from generate_prompts import generate_prompt
 from diffusers import AutoPipelineForText2Image
 from io import BytesIO
 import gradio as gr
-from concurrent.futures import ProcessPoolExecutor
+from multiprocessing import Pool, cpu_count
 
 # Load the model once outside of the function
 print("Loading the model...")
@@ -43,19 +43,14 @@ 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 for paragraph {paragraph_number}: {combined_sentence}")
-        prompt = generate_prompt(combined_sentence, character_dict, selected_style)
+        prompt = generate_prompt(combined_sentence, sentence_mapping, character_dict, selected_style)
         prompts.append((paragraph_number, prompt))
         print(f"Generated prompt for paragraph {paragraph_number}: {prompt}")
 
-    # Set max_workers to the total number of prompts
-    max_workers = len(prompts)
-
-    # Generate images for each prompt in parallel using multiprocessing
-    with ProcessPoolExecutor(max_workers=max_workers) as executor:
-        loop = asyncio.get_running_loop()
-        tasks = [loop.run_in_executor(executor, generate_image, prompt, f"Prompt {paragraph_number}") for paragraph_number, prompt in prompts]
-        print("Tasks created for image generation.")
-        responses = await asyncio.gather(*tasks)
+    # Use multiprocessing Pool to generate images in parallel
+    with Pool(cpu_count()) as pool:
+        tasks = [(prompt, f"Prompt {paragraph_number}") for paragraph_number, prompt in prompts]
+        responses = pool.starmap(generate_image, tasks)
         print("Responses received from image generation tasks.")
 
     images = {paragraph_number: response for (paragraph_number, _), response in zip(prompts, responses)}