Update app.py

app.py

@@ -1,3 +1,4 @@
+import asyncio
 import gradio as gr
 from diffusers import AutoPipelineForText2Image
 from generate_prompts import generate_prompt
@@ -5,10 +6,10 @@ from generate_prompts import generate_prompt
 # Load the model once outside of the function
 model = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo")
 
-def generate_image(prompt, prompt_name):
+async def generate_image(prompt, prompt_name):
     try:
         print(f"Generating image for {prompt_name}")
-        output = model(prompt=prompt, num_inference_steps=1, guidance_scale=0.0)
+        output = await 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)}")
@@ -17,24 +18,52 @@ 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 = [generate_image(prompt, f"Prompt {i}") for i, prompt in enumerate(prompts)]
+    image_bytes_list = async_image_generation(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
 
 # Gradio Interface
+def update_images(sentence_mapping, character_dict, selected_style):
+    prompts = generate_prompt(sentence_mapping, character_dict, selected_style)
+    image_bytes_list = async_image_generation(prompts)
+    return image_bytes_list
+
 with gr.Blocks() as demo:
-    with gr.Row():
-        with gr.Column():
-            sentence_mapping_input = gr.Textbox(label="Sentence Mapping")
-            character_dict_input = gr.Textbox(label="Character Dictionary")
-            selected_style_input = gr.Textbox(label="Selected Style")
-            submit_btn = gr.Button(value='Submit')
-            prompt_responses = []  # Empty list for dynamic addition of Image components
-            submit_btn.click(fn=gradio_interface,
-                             inputs=[sentence_mapping_input, character_dict_input, selected_style_input],
-                             outputs=prompt_responses)
+    sentence_mapping_input = gr.Textbox(label="Sentence Mapping")
+    character_dict_input = gr.Textbox(label="Character Dictionary")
+    selected_style_input = gr.Textbox(label="Selected Style")
+    
+    output_images = gr.Gallery(label="Generated Images").style(grid=[2], height=300)
+
+    def generate_and_update_images(sentence_mapping, character_dict, selected_style):
+        image_bytes_list = update_images(sentence_mapping, character_dict, selected_style)
+        return [gr.Image.update(value=img_bytes) if img_bytes else gr.Image.update(value=None) for img_bytes in image_bytes_list]
+
+    sentence_mapping_input.change(fn=generate_and_update_images, 
+                                  inputs=[sentence_mapping_input, character_dict_input, selected_style_input],
+                                  outputs=output_images)
+    character_dict_input.change(fn=generate_and_update_images, 
+                                inputs=[sentence_mapping_input, character_dict_input, selected_style_input],
+                                outputs=output_images)
+    selected_style_input.change(fn=generate_and_update_images, 
+                                inputs=[sentence_mapping_input, character_dict_input, selected_style_input],
+                                outputs=output_images)
 
 if __name__ == "__main__":
     demo.launch()