Update app.py

app.py

@@ -1,5 +1,3 @@
-import asyncio
-import json
 import gradio as gr
 from diffusers import AutoPipelineForText2Image
 from generate_prompts import generate_prompt
@@ -7,10 +5,10 @@ from generate_prompts import generate_prompt
 # Load the model once outside of the function
 model = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo")
 
-async def generate_image(prompt, prompt_name):
+def generate_image(prompt, prompt_name):
     try:
         print(f"Generating image for {prompt_name}")
-        output = await model(prompt=prompt, num_inference_steps=1, guidance_scale=0.0)
+        output = model(prompt=prompt, num_inference_steps=1, guidance_scale=0.0)
         image = output.images[0]
         img_bytes = image.tobytes()
         print(f"Image bytes length for {prompt_name}: {len(img_bytes)}")
@@ -19,23 +17,9 @@ async def generate_image(prompt, prompt_name):
         print(f"Error generating image for {prompt_name}: {e}")
         return None
 
-async def queue_image_calls(prompts):
-    tasks = [generate_image(prompts[i], f"Prompt {i}") for i in range(len(prompts))]
-    responses = await asyncio.gather(*tasks)
-    return responses
-
-def async_image_generation(prompts):
-    try:
-        loop = asyncio.get_running_loop()
-    except RuntimeError:
-        loop = asyncio.new_event_loop()
-        asyncio.set_event_loop(loop)
-    results = loop.run_until_complete(queue_image_calls(prompts))
-    return results
-
 def gradio_interface(sentence_mapping, character_dict, selected_style):
     prompts = generate_prompt(sentence_mapping, character_dict, selected_style)
-    image_bytes_list = async_image_generation(prompts)
+    image_bytes_list = [generate_image(prompt, f"Prompt {i}") for i, prompt in enumerate(prompts)]
     outputs = [gr.Image.update(value=img_bytes) if img_bytes else gr.Image.update(value=None) for img_bytes in image_bytes_list]
     return outputs
 
@@ -48,7 +32,6 @@ with gr.Blocks() as demo:
             selected_style_input = gr.Textbox(label="Selected Style")
             submit_btn = gr.Button(value='Submit')
             prompt_responses = []  # Empty list for dynamic addition of Image components
-            demo.load(fn=lambda x: x, inputs=[], outputs=prompt_responses)
             submit_btn.click(fn=gradio_interface,
                              inputs=[sentence_mapping_input, character_dict_input, selected_style_input],
                              outputs=prompt_responses)