Update app.py

app.py

@@ -1,20 +1,43 @@
 import gradio as gr
-import torch
+import asyncio
 from diffusers import AutoPipelineForText2Image
 from io import BytesIO
 from generate_propmts import generate_prompt
-import asyncio
+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
 
 # 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()
@@ -23,13 +46,15 @@ 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
@@ -37,7 +62,6 @@ 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]
@@ -45,11 +69,12 @@ async def process_prompt(sentence_mapping, character_dict, selected_style):
 
     # Map results back to paragraphs
     for i, (paragraph_number, _) in enumerate(prompts):
-        images[paragraph_number] = results[i]
+        if i < len(results):
+            images[paragraph_number] = results[i]
 
     return images
 
-# Gradio interface with high concurrency limit
+# Define Gradio interface with high concurrency limit
 gradio_interface = gr.Interface(
     fn=process_prompt,
     inputs=[