Create app.py

app.py

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+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+# Model setup
+model_name = "EleutherAI/gpt-neo-125M"  # Lightweight model for Spaces
+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):
+    inputs = tokenizer(prompt, return_tensors="pt")
+    outputs = model.generate(
+        **inputs,
+        max_length=max_length,
+        temperature=temperature,
+        top_p=top_p,
+        do_sample=True,
+        pad_token_id=tokenizer.eos_token_id
+    )
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# Global variables to store hierarchical content
+global_synopsis = ""
+global_chapters = ""
+
+# Generation functions
+def generate_synopsis(topic):
+    global global_synopsis
+    prompt = f"Write a synopsis for a story about {topic}: "
+    global_synopsis = generate_text(prompt, max_length=50)
+    return global_synopsis
+
+def generate_chapters():
+    global global_synopsis, global_chapters
+    if not global_synopsis:
+        return "Please generate a synopsis first."
+    prompt = f"Based on this synopsis: {global_synopsis}, divide the story into 3 chapters with brief descriptions for each."
+    global_chapters = generate_text(prompt, max_length=150)
+    return global_chapters
+
+def expand_chapter(chapter_number):
+    global global_chapters
+    if not global_chapters:
+        return "Please generate chapters first."
+    chapters = global_chapters.split("\n")
+    if chapter_number <= 0 or chapter_number > len(chapters):
+        return f"Select a number between 1 and {len(chapters)}."
+    prompt = f"Expand and describe Chapter {chapter_number} in more detail: {chapters[chapter_number - 1]}"
+    return generate_text(prompt, max_length=200)
+
+# Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("## AI Hierarchical Story Generator")
+    
+    with gr.Tab("Generate Synopsis"):
+        topic_input = gr.Textbox(label="Enter the story's main topic")
+        synopsis_output = gr.Textbox(label="Generated Synopsis", interactive=False)
+        synopsis_button = gr.Button("Generate Synopsis")
+    
+    with gr.Tab("Generate Chapters"):
+        chapters_output = gr.Textbox(label="Generated Chapters", interactive=False)
+        chapters_button = gr.Button("Generate Chapters")
+    
+    with gr.Tab("Expand Chapter"):
+        chapter_input = gr.Number(label="Chapter Number", precision=0)
+        chapter_detail_output = gr.Textbox(label="Expanded Chapter", interactive=False)
+        chapter_button = gr.Button("Expand Chapter")
+    
+    # Connect functions to UI
+    synopsis_button.click(generate_synopsis, inputs=topic_input, outputs=synopsis_output)
+    chapters_button.click(generate_chapters, outputs=chapters_output)
+    chapter_button.click(expand_chapter, inputs=chapter_input, outputs=chapter_detail_output)
+
+# Launch the app
+demo.launch()