Update app.py

app.py

@@ -1,10 +1,9 @@
+from generate_propmts import generate_prompt
 import gradio as gr
 import torch
-from diffusers import StableDiffusionXLPipeline, AutoPipelineForText2Image
+from diffusers import AutoPipelineForText2Image
 from io import BytesIO
 import asyncio
-from generate_propmts import generate_prompt
-from concurrent.futures import ThreadPoolExecutor, as_completed
 
 # Load the model once outside of the function
 model = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo")
@@ -12,7 +11,7 @@ model = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo")
 async def generate_image(prompt):
     try:
         # Generate an image based on the prompt
-        output = await asyncio.to_thread(model, prompt=prompt, num_inference_steps=50, guidance_scale=7.5)
+        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
@@ -49,10 +48,15 @@ async def process_prompt(sentence_mapping, character_dict, selected_style):
         if i < len(results):
             images[paragraph_number] = results[i]
         else:
-            print(f"Error: No image generated for paragraph {paragraph_number}")
+            print(f"Error: No result for paragraph {paragraph_number}")
 
     return images
 
+# Helper function to generate a prompt based on the input
+def generate_prompt(combined_sentence, sentence_mapping, character_dict, selected_style):
+    characters = " ".join(character_dict.values())
+    return f"Make an illustration in {selected_style} style from: {characters}. {combined_sentence}"
+
 # Gradio interface with high concurrency limit
 gradio_interface = gr.Interface(
     fn=process_prompt,
@@ -62,8 +66,9 @@ gradio_interface = gr.Interface(
         gr.Dropdown(["oil painting", "sketch", "watercolor"], label="Selected Style")
     ],
     outputs="json",
-    concurrency_count=20  # Set a high concurrency limit
+    concurrency_limit=20  # Set a high concurrency limit
 )
 
 if __name__ == "__main__":
     gradio_interface.launch()  # No need for share=True for local testing
+