Update app.py

app.py

@@ -1,12 +1,12 @@
 import os
-import asyncio
+import concurrent.futures
 from generate_prompts import generate_prompt
 from diffusers import AutoPipelineForText2Image
 from io import BytesIO
 import gradio as gr
-from concurrent.futures import ProcessPoolExecutor
+import json
 
-# Load the model once outside of the function
+# Load the model once globally
 print("Loading the model...")
 model = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo")
 print("Model loaded successfully.")
@@ -25,21 +25,20 @@ def generate_image(prompt, prompt_name):
                 image.save(buffered, format="JPEG")
                 image_bytes = buffered.getvalue()
                 print(f"Image bytes length for {prompt_name}: {len(image_bytes)}")
-                return image_bytes
+                return prompt_name, image_bytes
             except Exception as e:
                 print(f"Error saving image for {prompt_name}: {e}")
-                return None
+                return prompt_name, None
         else:
             raise Exception(f"No images returned by the model for {prompt_name}.")
     except Exception as e:
         print(f"Error generating image for {prompt_name}: {e}")
-        return None
+        return prompt_name, None
 
-async def queue_api_calls(sentence_mapping, character_dict, selected_style):
-    print(f"queue_api_calls invoked with sentence_mapping: {sentence_mapping}, character_dict: {character_dict}, selected_style: {selected_style}")
+def process_prompts(sentence_mapping, character_dict, selected_style):
+    print(f"process_prompts called with sentence_mapping: {sentence_mapping}, character_dict: {character_dict}, selected_style: {selected_style}")
+    
     prompts = []
-
-    # Generate prompts for each paragraph
     for paragraph_number, sentences in sentence_mapping.items():
         combined_sentence = " ".join(sentences)
         print(f"combined_sentence for paragraph {paragraph_number}: {combined_sentence}")
@@ -47,38 +46,26 @@ 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}")
 
-    # Set max_workers to the total number of prompts
-    max_workers = len(prompts)
+    num_prompts = len(prompts)
+    print(f"Number of prompts: {num_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)
-        print("Responses received from image generation tasks.")
+    with concurrent.futures.ThreadPoolExecutor(max_workers=num_prompts) as executor:
+        tasks = {executor.submit(generate_image, prompt, f"Prompt {paragraph_number}"): paragraph_number for paragraph_number, prompt in prompts}
+        results = {tasks[future]: future.result() for future in concurrent.futures.as_completed(tasks)}
 
-    images = {paragraph_number: response for (paragraph_number, _), response in zip(prompts, responses)}
+    images = {prompt_name: image for prompt_name, (prompt_name_key, image) in results.items()}
     print(f"Images generated: {images}")
     return images
 
 def process_prompt(sentence_mapping, character_dict, selected_style):
     print(f"process_prompt called with sentence_mapping: {sentence_mapping}, character_dict: {character_dict}, selected_style: {selected_style}")
-    try:
-        # See if there is a loop already running. If there is, reuse it.
-        loop = asyncio.get_running_loop()
-    except RuntimeError:
-        # Create new event loop if one is not running
-        loop = asyncio.new_event_loop()
-        asyncio.set_event_loop(loop)
-    print("Event loop created.")
-
-    # This sends the prompts to function that sets up the async calls. Once all the calls to the API complete, it returns a list of the gr.Textbox with value= set.
-    cmpt_return = loop.run_until_complete(queue_api_calls(sentence_mapping, character_dict, selected_style))
-    print(f"process_prompt completed with return value: {cmpt_return}")
-    return cmpt_return
+    # Check if inputs are already in dict form
+    if isinstance(sentence_mapping, str):
+        sentence_mapping = json.loads(sentence_mapping)
+    if isinstance(character_dict, str):
+        character_dict = json.loads(character_dict)
+    return process_prompts(sentence_mapping, character_dict, selected_style)
 
-# Gradio interface with high concurrency limit
 gradio_interface = gr.Interface(
     fn=process_prompt,
     inputs=[