Update app.py

app.py

@@ -1,43 +1,19 @@
 import gradio as gr
-import asyncio
+import torch
 from diffusers import AutoPipelineForText2Image
 from io import BytesIO
-from generate_propmts import generate_prompt
-import threading
-
-# Define the Scheduler class
-class Scheduler:
-    def __init__(self):
-        self._step = threading.local()
-        self._step.step = None
-
-    def _init_step_index(self):
-        self._step.step = 0
-
-    @property
-    def step(self):
-        return self._step.step
-
-    def step_process(self):
-        if self._step.step is None:
-            self._init_step_index()
-        self._step.step += 1
+import asyncio
 
 # Load the model once outside of the function
 model = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo")
 
-# Create a Scheduler instance
-scheduler = Scheduler()
-
 async def generate_image(prompt):
     try:
-        # Update the scheduler step
-        scheduler.step_process()
-        print(f"Current step: {scheduler.step}")
-
         # Generate an image based on the prompt
         output = await asyncio.to_thread(model, prompt=prompt, num_inference_steps=1, guidance_scale=0.0)
-        
+        print(f"Model output: {output}")
+
+        # Check if the model returned images
         if isinstance(output.images, list) and len(output.images) > 0:
             image = output.images[0]
             buffered = BytesIO()
@@ -46,15 +22,13 @@ async def generate_image(prompt):
             return image_bytes
         else:
             raise Exception("No images returned by the model.")
-    except IndexError as e:
-        print(f"IndexError: {e}")
-        return None
     except Exception as e:
         print(f"Error generating image: {e}")
         return None
 
 async def process_prompt(sentence_mapping, character_dict, selected_style):
     images = {}
+    print(f'sentence_mapping: {sentence_mapping}, character_dict: {character_dict}, selected_style: {selected_style}')
     prompts = []
 
     # Generate prompts for each paragraph
@@ -62,6 +36,7 @@ async def process_prompt(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(f"Generated prompt for paragraph {paragraph_number}: {prompt}")
 
     # Create tasks for all prompts and run them concurrently
     tasks = [generate_image(prompt) for _, prompt in prompts]
@@ -74,7 +49,7 @@ async def process_prompt(sentence_mapping, character_dict, selected_style):
 
     return images
 
-# Define Gradio interface with high concurrency limit
+# Gradio interface with high concurrency limit
 gradio_interface = gr.Interface(
     fn=process_prompt,
     inputs=[