Update app.py

app.py

@@ -2,28 +2,29 @@ import os
 import asyncio
 from concurrent.futures import ProcessPoolExecutor
 from io import BytesIO
+from PIL import Image
 from diffusers import StableDiffusionPipeline
 import gradio as gr
 from generate_prompts import generate_prompt
 
 # Load the model once at the start
 print("Loading the Stable Diffusion model...")
-model = StableDiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo")
-print("Model loaded successfully.")
-
-def truncate_prompt(prompt, max_length=77):
-    tokens = prompt.split()
-    if len(tokens) > max_length:
-        prompt = " ".join(tokens[:max_length])
-    return prompt
+try:
+    model = StableDiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo")
+    print("Model loaded successfully.")
+except Exception as e:
+    print(f"Error loading model: {e}")
+    model = None
 
 def generate_image(prompt, prompt_name):
     try:
+        if model is None:
+            raise ValueError("Model not loaded properly.")
+        
         print(f"Generating image for {prompt_name} with prompt: {prompt}")
-        truncated_prompt = truncate_prompt(prompt)
-        output = model(prompt=truncated_prompt, num_inference_steps=1, guidance_scale=0.0)
+        output = model(prompt=prompt, num_inference_steps=1, guidance_scale=0.0)
         print(f"Model output for {prompt_name}: {output}")
-        
+
         if output and hasattr(output, 'images') and output.images:
             print(f"Image generated for {prompt_name}")
             image = output.images[0]
@@ -40,6 +41,7 @@ def generate_image(prompt, prompt_name):
 
 async def queue_api_calls(sentence_mapping, character_dict, selected_style):
     print("Starting to queue API calls...")
+    print(f'sentence_mapping"{sentence_mapping}')
     prompts = []
     for paragraph_number, sentences in sentence_mapping.items():
         combined_sentence = " ".join(sentences)