Update app.py

app.py

@@ -14,8 +14,7 @@ from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5Tokeniz
 from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
 import gradio as gr
 from accelerate import Accelerator
-from huggingface_hub import spaces
-
+from huggingface_hub import spaces  # Ensure this import is correct
 
 # Instantiate the Accelerator
 accelerator = Accelerator()
@@ -61,7 +60,7 @@ os.makedirs(output_dir, exist_ok=True)
 
 # Function to generate a detailed visual description prompt
 def generate_description_prompt(subject, user_prompt, text_generator):
-    prompt = f"write concise vivid visual description enclosed in brackets like [ <description> ] less than 50 words of {user_prompt} different from {subject}. "
+    prompt = f"write concise vivid visual description enclosed in brackets like [ <description> ] less than 100 words of {user_prompt} different from {subject}. "
     try:
         generated_text = text_generator(prompt, max_length=160, num_return_sequences=1, truncation=True)[0]['generated_text']
         generated_description = re.sub(rf'{re.escape(prompt)}\s*', '', generated_text).strip()  # Remove the prompt from the generated text
@@ -131,6 +130,8 @@ def generate_and_store_descriptions(user_prompt, batch_size=100, max_iterations=
             if iteration_count % 3 == 0:
                 parsed_descriptions = parse_descriptions(clean_description)
                 parsed_descriptions_queue.extend(parsed_descriptions)
+                # Return the parsed descriptions to update the Gradio UI
+                return list(parsed_descriptions_queue)
 
         iteration_count += 1