storygenerator 0.1.0

Gemini-based story generator for children's books

DailyStories Generator

A Python library for generating children's storybooks using Google's Gemini AI.

Installation

# From PyPI
pip install dailystories-generator

# Or with uv
uv pip install dailystories-generator

Building and Publishing

Build

./build.sh

This creates distribution files in dist/ directory.

Publish to PyPI

1. Get a PyPI API token from https://pypi.org/manage/account/token/
2. Set the token:

   export PYPI_TOKEN="pypi-your-token-here"
   

3. Publish:

   ./publish.sh
   

The publish script will automatically build the package before publishing.

Quick Test

# Set your API key
export GOOGLE_API_KEY="your-gemini-api-key-here"

# Run a simple test
uv run python test.py

# Or test outline-only (faster)
uv run python test.py --outline-only

# Or test without images (faster)
uv run python test.py --no-images

Usage

from dailystories_generator import StoryGenerator, GenerationRequest, UpdateType

async def on_update(update):
    print(f"Update: {update.type}")
    if update.type == UpdateType.PAGE_COMPLETE:
        print(f"Page {update.data['page_number']} completed")

generator = StoryGenerator(gemini_api_key="your-api-key")

request = GenerationRequest(
    title="The Adventure Begins",
    summary="A tale of courage and friendship",
    num_pages=10,
    child_name="Alex",
    child_age=6,
    language="English",
    illustration_style="watercolor",
    generate_images=True,
)

story = await generator.generate(request, on_update=on_update)
print(f"Story generated with {len(story.pages)} pages")

Features

• Async story generation with Gemini AI
• Optional image generation
• Support for multiple reference images
• Type-safe with full type annotations
• Callback-based progress updates
• Prompt optimization system for iterative improvement

Prompt Optimization

The library includes a powerful prompt optimization system that automatically improves story generation prompts through iterative evaluation and feedback.

How It Works

1. Generate story content (outline or pages)
2. Evaluate across 11 quality categories (1-5 scale with explanations)
3. Improve the prompt based on evaluation feedback
4. Validate that all template placeholders are preserved
5. Track scores in statistics.csv for analysis
6. Repeat for N iterations

Evaluation Categories

The system evaluates generated content on:

• creativity - Originality and imaginativeness
• age_appropriateness - Suitable content and vocabulary for target age
• coherence - Logical flow and narrative consistency
• engagement - How captivating for young readers
• language_quality - Grammar and writing style
• plot_structure - Clear beginning/middle/end
• character_development - Growth and relatability
• character_introduction - How well characters are established
• emotional_resonance - Emotional depth and impact
• pacing - Story rhythm and transitions
• tone_consistency - Maintaining age-appropriate tone

Running Optimization

# Set your API key
export GOOGLE_API_KEY="your-gemini-api-key-here"

# Optimize the outline prompt (50 iterations)
uv run python optimize_prompts.py --mode outline --iterations 50 \
  --child-name Ludwig --child-age 6 \
  --title "The Magical Adventure" \
  --summary "A child discovers magic and goes on an adventure" \
  --language Norwegian

# Optimize the page generation prompt (20 iterations)
uv run python optimize_prompts.py --mode pages --iterations 20 \
  --child-name Emma --child-age 7 \
  --num-pages 5 \
  --language English

Command Line Options

• --mode - Type of prompt to optimize: outline or pages (required)
• --iterations - Number of optimization iterations (default: 10)
• --child-name - Child's name for test story (default: Alex)
• --child-age - Child's age for test story (default: 6)
• --title - Story title (default: The Magical Adventure)
• --summary - Story theme/summary
• --num-pages - Number of pages (default: 5)
• --language - Story language (default: English)

Output

The optimization system produces:

1. Updated prompt templates in prompt_templates/

   • story_outline_prompt.txt
   • story_page_prompt.txt

2. Statistics CSV (statistics.csv) with columns:

   • timestamp, mode, iteration, prompt_version
   • All 11 category scores (1-5)
   • average_score

3. Console output showing:

   • Current iteration progress
   • Evaluation scores per category
   • Improvement suggestions
   • Placeholder validation results

Analyzing Results

Use the statistics.csv file to plot score trajectories over iterations:

import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv('statistics.csv')
df_outline = df[df['mode'] == 'outline']

# Plot trajectory for each category
for category in ['creativity', 'engagement', 'coherence']:
    plt.plot(df_outline['iteration'], df_outline[category], label=category)

plt.xlabel('Iteration')
plt.ylabel('Score (1-5)')
plt.legend()
plt.title('Prompt Optimization Progress')
plt.show()

Automatic Retry on Placeholder Loss

The system automatically validates that all template placeholders (e.g., {child_name}, {story_so_far}) are preserved during optimization. If the LLM forgets a placeholder, it receives clear feedback and retries up to 5 times before failing.

This ensures the system can run autonomously for hours without manual intervention.