Update app.py

app.py

@@ -9,7 +9,7 @@ tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForCausalLM.from_pretrained(model_name).to("cpu")  # CPU-friendly for Spaces
 
 # Text generation function
-def generate_text(prompt, max_length=100, temperature=0.7, top_p=0.9):
+def generate_text(prompt, max_length=500, temperature=0.7, top_p=0.9):
     inputs = tokenizer(prompt, return_tensors="pt")
     outputs = model.generate(
         **inputs,
@@ -28,8 +28,8 @@ global_chapters = ""
 # Generation functions
 def generate_synopsis(topic):
     global global_synopsis
-    prompt = f"Write a brief synopsis for a story about {topic}: "
-    global_synopsis = generate_text(prompt, max_length=100)
+    prompt = f"Write a brief synopsis for a story about {topic}: . The max number of characters must be 500"
+    global_synopsis = generate_text(prompt, max_length=500)
     return global_synopsis
 
 def generate_chapters():
@@ -37,7 +37,8 @@ def generate_chapters():
     if not global_synopsis:
         return "Please generate a synopsis first."
     prompt = f'''Based on this synopsis for a book: {global_synopsis}. Divide the story into 4 chapters with brief descriptions for each. 
-    Enumerate every chapter created followed by its description and make the first chapter sound like an introduction and the last may sound as the epilogue'''
+    Enumerate every chapter created followed by its description and make the first chapter sound like an introduction and the last may sound as the epilogue.
+    The max number of character for every pair title, description must be 500'''
     global_chapters = generate_text(prompt, max_length=2000)
     return global_chapters
 
@@ -50,7 +51,7 @@ def expand_chapter(chapter_number):
         return f"Select a number between 1 and {len(chapters)}."
     prompt = f'''Knowing this synopsis for a book: {global_synopsis}. Expand and describe Chapter {chapter_number} 
     in more detail, the title and current brief description of this chapter is: {chapters[chapter_number - 1]}'''
-    return generate_text(prompt, max_length=200)
+    return generate_text(prompt, max_length=500)
 
 # Gradio interface
 with gr.Blocks() as demo: