GRADIO_UI_GUIDE.md

StoryBox Gradio UI + Multilingual Support

This guide covers building a web UI for StoryBox, adding Hindi/multilingual support, and using it as a synthetic data generator.

---

1. Gradio Web UI

Create ui/app.py:

"""
StoryBox Web UI — built with Gradio.
Supports story generation, persona editing, world building, and multilingual output.
"""
import json
import os
import sys
from pathlib import Path

import gradio as gr

# Add storybox to path
sys.path.insert(0, str(Path(__file__).resolve().parents[1]))

from reverie.config.config import Config
from reverie.environment.world import World
from reverie.persona.persona import Persona
from reverie.manager.persona_manager import persona_manager
from reverie.manager.datetime_manager import datetime_manager
from reverie.agent.storyteller import Storyteller

# ------------------------------------------------------------------
# Story generation pipeline
# ------------------------------------------------------------------
def generate_story(
    story_setting: str,
    language: str,
    num_days: int,
    num_personas: int,
    model_provider: str,
    model_name: str,
    temperature: float,
    progress=gr.Progress(),
):
    """
    Run full StoryBox pipeline and return the generated story.
    """
    # Override config
    Config.story_name = story_setting
    Config.story_dir = f"{Config.data_dir}/{story_setting}"
    Config.max_iteration = 24 * num_days
    Config.llm_model_name = model_name
    Config.temperature = temperature

    # Load world and personas
    world = World()
    world.load_file(f"{Config.story_dir}/world.yaml")

    persona_folder = f"{Config.story_dir}/personas"
    persona_names = sorted(os.listdir(persona_folder))[:num_personas]
    for name in persona_names:
        p = Persona(name, f"{persona_folder}/{name}")
        persona_manager.add_persona(p)

    # Run simulation
    all_personas = persona_manager.get_all_personas()
    for i in range(Config.max_iteration):
        progress(i / Config.max_iteration, desc=f"Simulating hour {i+1}/{Config.max_iteration}")
        new_day = 'First day' if i == 0 else 'New day' if datetime_manager.is_new_day() else False
        for persona in all_personas:
            # Simplified sync call for UI
            import asyncio
            asyncio.run(persona.step(world, new_day=new_day))
        datetime_manager.advance_datetime(hours=1)

    # Generate story
    progress(0.9, desc="Writing story...")
    storyteller = Storyteller(Config.story_dir, Config.output_dir)
    storyteller.summarize_daily_by_persona()
    storyteller.generate_story_title()
    storyteller.generate_story_type()
    storyteller.generate_story_background()
    storyteller.generate_story_themes()
    storyteller.generate_story_chapters()
    storyteller.generate_story_conflicts()
    storyteller.generate_story_plot_points()
    storyteller.generate_story()
    storyteller.save()

    story = storyteller.get_story(with_title=True)

    # Translate if needed
    if language != "English":
        story = translate_story(story, language)

    return story


def translate_story(text: str, target_lang: str) -> str:
    """Translate generated story using an LLM call."""
    from reverie.common.llm import get_chat_model
    model = get_chat_model(Config.llm_model_name, Config.temperature)
    prompt = (
        f"Translate the following story into {target_lang}. "
        f"Preserve the narrative style, character voices, and emotional tone. "
        f"Do not summarize — translate the full text:\n\n{text[:4000]}"
    )
    resp = model.invoke(prompt)
    return resp.content


# ------------------------------------------------------------------
# Gradio Interface
# ------------------------------------------------------------------
def build_ui():
    with gr.Blocks(title="StoryBox — AI Story Generator") as demo:
        gr.Markdown("# 📖 StoryBox")
        gr.Markdown("Generate long-form stories with multi-agent simulation.")

        with gr.Row():
            with gr.Column(scale=1):
                story_setting = gr.Dropdown(
                    choices=[f"story{i:02d}" for i in range(1, 21)],
                    value="story01",
                    label="Story Setting"
                )
                language = gr.Dropdown(
                    choices=["English", "Hindi", "Spanish", "French", "Arabic", "Chinese", "Japanese"],
                    value="English",
                    label="Output Language"
                )
                num_days = gr.Slider(1, 14, value=3, step=1, label="Simulation Days")
                num_personas = gr.Slider(2, 6, value=4, step=1, label="Number of Characters")

                model_provider = gr.Radio(
                    choices=["OpenAI", "Ollama", "MLX (Apple)", "NVIDIA NIM"],
                    value="Ollama",
                    label="Model Provider"
                )
                model_name = gr.Textbox(value="llama3.1:8b", label="Model Name")
                temperature = gr.Slider(0.0, 1.5, value=0.8, step=0.05, label="Temperature")

                generate_btn = gr.Button("🚀 Generate Story", variant="primary")

            with gr.Column(scale=2):
                output_story = gr.Textbox(
                    label="Generated Story",
                    lines=30,
                    max_lines=50,
                    show_copy_button=True,
                )
                output_metadata = gr.JSON(label="Story Metadata")

        generate_btn.click(
            fn=generate_story,
            inputs=[story_setting, language, num_days, num_personas,
                    model_provider, model_name, temperature],
            outputs=[output_story, output_metadata],
        )

        gr.Markdown("---")
        gr.Markdown("### 💡 Tips")
        gr.Markdown("""
        - **More days** = richer story but longer generation time
        - **Lower temperature** = more predictable, coherent stories
        - **MLX on Apple Silicon** is fastest for local inference
        - **Hindi stories**: Select Hindi output + use a multilingual base model
        """)

    return demo


if __name__ == "__main__":
    demo = build_ui()
    demo.launch(server_name="0.0.0.0", server_port=7860, share=False)

Run the UI

pip install gradio
cd ui
python app.py
# Open http://localhost:7860

---

2. Hindi / Multilingual Story Generation

The Challenge

StoryBox generates stories in English by default because:

1. Prompts are in English
2. Base model (GPT-4o-mini) is English-dominant
3. Persona descriptions are in English

Solution A: Post-Generation Translation (Easiest)

After generating the English story, translate it:

from reverie.common.llm import get_chat_model

def translate_to_hindi(english_story: str) -> str:
    model = get_chat_model("nvidia/meta/llama-3.1-8b-instruct", 0.3)
    prompt = f"""
    Translate this English story into fluent, natural Hindi.
    Preserve all character names, dialogue style, and emotional depth.
    Use Devanagari script. Do not summarize — translate the full text:

    {english_story}
    """
    return model.invoke(prompt).content

Pros: Simple, works with any model
Cons: May lose cultural nuance, idioms don't translate well

Solution B: Native Hindi Generation (Better)

Use a Hindi-capable base model and Hindi prompts:

# config.py
llm_model_name = 'nvidia/meta/llama-3.1-8b-instruct'  # Multilingual
# OR
llm_model_name = 'qwen2.5-mlx'  # Strong multilingual

Create Hindi prompt templates in reverie/prompts/prompt-hi/:

# generate_first_daily_plan.txt (Hindi version)
Variables:
!<INPUT 0>! -- पात्र की पहचान
!<INPUT 1>! -- आज की तारीख
...

<commentblockmarker>###</commentblockmarker>
आप एक कहानीकार हैं। नीचे दिए गए पात्र के लिए दैनिक योजना बनाएं:
!<INPUT 0>!

आज की तारीख: !<INPUT 1>!
...

Pros: Culturally authentic, natural Hindi
Cons: Need to translate all 30+ prompts

Solution C: Hybrid — English Simulation + Hindi Storyteller (Best)

Keep the sandbox simulation in English (characters plan, chat, move in English), but make the Storyteller Agent write in Hindi:

# In storyteller.py, modify generate_plot_content():

prompt_inputs = [
    self.story_title,
    self.story_type,
    "यह कहानी हिंदी में लिखी गई है।",  # Hindi instruction
    ...
]

This is the recommended approach — simulation logic stays universal, output language is flexible.

---

3. Synthetic Data Generation Pipeline

StoryBox is excellent for generating synthetic training data:

Use Case 1: Children's Stories Dataset

"""
Generate 10,000 synthetic children's stories for fine-tuning.
"""
import json
from pathlib import Path

def generate_synthetic_dataset(
    num_stories: int = 1000,
    output_file: str = "synthetic_stories.jsonl",
    language: str = "English",
):
    stories = []
    for i in range(num_stories):
        # Randomize story setting
        story_id = f"story{(i % 20) + 1:02d}"
        Config.story_name = story_id

        # Run StoryBox (simplified)
        story_text = run_storybox_simulation()

        if language != "English":
            story_text = translate_story(story_text, language)

        stories.append({
            "id": f"story_{i:06d}",
            "language": language,
            "setting": story_id,
            "text": story_text,
            "word_count": len(story_text.split()),
            "metadata": {
                "model": Config.llm_model_name,
                "temperature": Config.temperature,
            }
        })

    # Save as JSONL
    with open(output_file, "w", encoding="utf-8") as f:
        for story in stories:
            f.write(json.dumps(story, ensure_ascii=False) + "\n")

    print(f"Generated {num_stories} stories → {output_file}")

Use Case 2: Instruction-Tuning Dataset

Convert stories into instruction-response pairs:

def stories_to_instruction_dataset(stories_file: str, output_file: str):
    """Convert stories into Alpaca-format instruction tuning data."""
    instructions = []

    with open(stories_file) as f:
        for line in f:
            story = json.loads(line)
            text = story["text"]

            # Generate multiple instructions per story
            instructions.append({
                "instruction": "Continue this story:",
                "input": text[:500],
                "output": text[500:1500],
            })
            instructions.append({
                "instruction": "Summarize this story in one paragraph:",
                "input": text,
                "output": generate_summary(text),
            })
            instructions.append({
                "instruction": "Describe the main character's personality:",
                "input": text,
                "output": extract_character_description(text),
            })

    with open(output_file, "w") as f:
        json.dump(instructions, f, indent=2, ensure_ascii=False)

Use Case 3: Low-Resource Language Corpus

For Hindi or other low-resource languages:

def generate_hindi_corpus(num_stories: int = 5000):
    """Generate synthetic Hindi stories for language model pre-training."""
    # Use a multilingual base model
    Config.llm_model_name = 'nvidia/meta/llama-3.1-8b-instruct'

    for i in range(num_stories):
        # Generate in English
        english_story = run_storybox_simulation()

        # Translate to Hindi
        hindi_story = translate_story(english_story, "Hindi")

        # Save
        with open(f"hindi_corpus/{i:06d}.txt", "w") as f:
            f.write(hindi_story)

        # Also save English-Hindi parallel
        with open(f"parallel_corpus/{i:06d}.json", "w") as f:
            json.dump({"en": english_story, "hi": hindi_story}, f, ensure_ascii=False)

---

4. Quality Evaluation for Synthetic Data

def evaluate_story_quality(story: str) -> dict:
    """Score a generated story on multiple dimensions."""
    scores = {}

    # Length check
    scores["length"] = len(story.split())
    scores["meets_length_target"] = scores["length"] > 5000

    # Coherence (simple heuristic)
    sentences = story.split(".")
    scores["avg_sentence_length"] = sum(len(s.split()) for s in sentences) / len(sentences)

    # Diversity (unique words / total words)
    words = story.lower().split()
    scores["lexical_diversity"] = len(set(words)) / len(words)

    # Repetition penalty
    from collections import Counter
    word_counts = Counter(words)
    scores["repetition_score"] = sum(1 for c in word_counts.values() if c > 5) / len(word_counts)

    return scores

---

5. Full Pipeline: English → Hindi Synthetic Dataset

# 1. Generate 1000 English stories
python scripts/generate_synthetic_dataset.py \
    --num-stories 1000 \
    --output en_stories.jsonl \
    --model llama3.1-8b-mlx

# 2. Translate to Hindi
python scripts/translate_dataset.py \
    --input en_stories.jsonl \
    --output hi_stories.jsonl \
    --target-language Hindi \
    --model nvidia/meta/llama-3.1-8b-instruct

# 3. Convert to instruction format
python scripts/to_instruction_format.py \
    --input hi_stories.jsonl \
    --output hi_instructions.json \
    --format alpaca

# 4. Push to HuggingFace
python scripts/upload_to_hub.py \
    --dataset hi_instructions.json \
    --repo-id yourname/hindi-synthetic-stories

---

6. UI Screenshots

The Gradio UI provides:

• Story Setting selector (20 pre-built worlds)
• Language dropdown (English, Hindi, etc.)
• Simulation controls (days, characters, temperature)
• Model provider picker (OpenAI, Ollama, MLX, NIM)
• Live progress bar during simulation
• Copy button for generated stories
• Metadata JSON with generation stats

---

7. Recommended Models by Language

Language	Recommended Model	Provider
English	gpt-4o-mini	OpenAI
Hindi	meta/llama-3.1-8b-instruct	NIM / Ollama
Hindi	qwen2.5-7b-instruct	MLX / Ollama
Arabic	ubc-nlp/arallama-2	HuggingFace
Chinese	qwen2.5-7b-instruct	MLX / Ollama
Japanese	elyza/elyza-japanese-llama-2	HuggingFace
Multilingual	meta/llama-3.1-8b-instruct	Any

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8. Repository Status

✅ Repository is now PRIVATE — https://huggingface.co/raazkumar/storybox-reproduction

Only you can access and clone it.