Update app.py

app.py

@@ -1,6 +1,5 @@
 from generate_prompts import generate_prompt
 import gradio as gr
-import torch
 from diffusers import AutoPipelineForText2Image
 from io import BytesIO
 import asyncio
@@ -8,11 +7,10 @@ import asyncio
 # Load the model once outside of the function
 model = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo")
 
-async def generate_image(prompt):
+async def generate_image(prompt, prompt_name):
     try:
-        # Generate an image based on the prompt
+        print(f"Generating image for {prompt_name}")
         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:
@@ -21,16 +19,15 @@ async def generate_image(prompt):
             try:
                 image.save(buffered, format="JPEG")
                 image_bytes = buffered.getvalue()
-                # Verify the image bytes
-                print(f"Image bytes length: {len(image_bytes)}")
+                print(f"Image bytes length for {prompt_name}: {len(image_bytes)}")
                 return image_bytes
             except Exception as e:
-                print(f"Error saving image: {e}")
+                print(f"Error saving image for {prompt_name}: {e}")
                 return None
         else:
-            raise Exception("No images returned by the model.")
+            raise Exception(f"No images returned by the model for {prompt_name}.")
     except Exception as e:
-        print(f"Error generating image: {e}")
+        print(f"Error generating image for {prompt_name}: {e}")
         return None
 
 async def process_prompt(sentence_mapping, character_dict, selected_style):
@@ -46,7 +43,7 @@ async def process_prompt(sentence_mapping, character_dict, selected_style):
         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]
+    tasks = [generate_image(prompt, f"Prompt {paragraph_number}") for paragraph_number, prompt in prompts]
     results = await asyncio.gather(*tasks)
 
     # Map results back to paragraphs
@@ -76,4 +73,4 @@ gradio_interface = gr.Interface(
 ).queue(default_concurrency_limit=20)
 
 if __name__ == "__main__":
-    gradio_interface.launch()  # No need for share=True for local testing
+    gradio_interface.launch()