Update app.py

app.py

@@ -6,12 +6,15 @@ from io import BytesIO
 import gradio as gr
 
 # Load the model once outside of the function
+print("Loading the model...")
 model = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo")
+print("Model loaded successfully.")
 
 def generate_image(prompt, prompt_name):
     try:
-        print(f"Generating response for {prompt_name}")
+        print(f"Generating response for {prompt_name} with prompt: {prompt}")
         output = model(prompt=prompt, num_inference_steps=1, guidance_scale=0.0)
+        print(f"Output for {prompt_name}: {output}")
 
         # Check if the model returned images
         if isinstance(output.images, list) and len(output.images) > 0:
@@ -32,13 +35,13 @@ def generate_image(prompt, prompt_name):
         return None
 
 async def queue_api_calls(sentence_mapping, character_dict, selected_style):
-    print(f'sentence_mapping: {sentence_mapping}, character_dict: {character_dict}, selected_style: {selected_style}')
+    print(f"queue_api_calls invoked with sentence_mapping: {sentence_mapping}, character_dict: {character_dict}, selected_style: {selected_style}")
     prompts = []
 
     # Generate prompts for each paragraph
     for paragraph_number, sentences in sentence_mapping.items():
         combined_sentence = " ".join(sentences)
-        print(f'combined_sentence: {combined_sentence}, character_dict: {character_dict}, selected_style: {selected_style}')
+        print(f"combined_sentence for paragraph {paragraph_number}: {combined_sentence}")
         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}")
@@ -46,12 +49,16 @@ async def queue_api_calls(sentence_mapping, character_dict, selected_style):
     # Generate images for each prompt in parallel
     loop = asyncio.get_running_loop()
     tasks = [loop.run_in_executor(None, generate_image, prompt, f"Prompt {paragraph_number}") for paragraph_number, prompt in prompts]
+    print("Tasks created for image generation.")
     responses = await asyncio.gather(*tasks)
-    
+    print("Responses received from image generation tasks.")
+
     images = {paragraph_number: response for (paragraph_number, _), response in zip(prompts, responses)}
+    print(f"Images generated: {images}")
     return images
 
 def process_prompt(sentence_mapping, character_dict, selected_style):
+    print(f"process_prompt called with sentence_mapping: {sentence_mapping}, character_dict: {character_dict}, selected_style: {selected_style}")
     try:
         # See if there is a loop already running. If there is, reuse it.
         loop = asyncio.get_running_loop()
@@ -59,9 +66,11 @@ def process_prompt(sentence_mapping, character_dict, selected_style):
         # Create new event loop if one is not running
         loop = asyncio.new_event_loop()
         asyncio.set_event_loop(loop)
+    print("Event loop created.")
 
     # This sends the prompts to function that sets up the async calls. Once all the calls to the API complete, it returns a list of the gr.Textbox with value= set.
     cmpt_return = loop.run_until_complete(queue_api_calls(sentence_mapping, character_dict, selected_style))
+    print(f"process_prompt completed with return value: {cmpt_return}")
     return cmpt_return
 
 # Gradio interface with high concurrency limit
@@ -76,4 +85,6 @@ gradio_interface = gr.Interface(
 )
 
 if __name__ == "__main__":
+    print("Launching Gradio interface...")
     gradio_interface.launch()
+    print("Gradio interface launched.")