Helix Framework Outline

The Helix Framework is designed to facilitate the generation of dynamic content, particularly recipes, using GPT models. It leverages a modular architecture, enabling flexible customization and extension.

Core Concepts:

• Recipes: Recipes represent the fundamental unit of content within the framework. They are defined as YAML files, each containing a set of ingredients, instructions, and optional metadata.
• GPT Scripts: GPT scripts are Python functions that utilize the OpenAI API to generate recipe content. They are responsible for formulating prompts, processing responses, and integrating generated content into recipes.
• Template Engine: The framework employs a template engine to render the final recipe output based on the generated content and recipe metadata. This allows for consistent formatting and visual presentation.
• Data Model: The framework defines a data model to structure and manage recipe information, ensuring efficient data storage and retrieval.

Components:

• recipe.py: This module provides classes and functions for defining, managing, and interacting with recipes.
  • Recipe class: Represents a single recipe, encapsulating its ingredients, instructions, and metadata. recipe.py
  • get_recipe function: Retrieves a recipe based on its identifier.
• gpt.py: This module defines GPT script functions responsible for generating recipe content.
  • generate_recipe_intro function: Generates an introductory paragraph for a recipe.
  • generate_recipe_steps function: Generates detailed instructions for a recipe.
• template.py: This module manages recipe templates, handling content rendering and formatting.
  • render_recipe function: Takes a recipe object and template file as input and renders the recipe in the desired format.
• data.py: This module provides functions for managing recipe data, including storage and retrieval.
  • load_recipes function: Loads recipes from a specified data source.
  • save_recipes function: Saves recipes to a specified data source.

Usage Examples:

• Creating a new recipe:

from recipe import Recipe
          
          new_recipe = Recipe(
              title="Chocolate Chip Cookies",
              ingredients=["1 cup butter", "1 cup sugar", "2 eggs", "2 cups flour", "1/2 cup chocolate chips"],
              instructions=[
                  "Cream together butter and sugar.",
                  "Beat in eggs, one at a time.",
                  "Stir in flour and chocolate chips.",
                  "Drop by rounded tablespoons onto ungreased baking sheets.",
                  "Bake at 375 degrees F (190 degrees C) for 10-12 minutes."
              ]
          )
          

• Generating recipe content using a GPT script:

from gpt import generate_recipe_intro
          
          intro_text = generate_recipe_intro(recipe_title="Chocolate Chip Cookies")
          print(intro_text)
          

• Rendering a recipe using a template:

from template import render_recipe
          
          rendered_recipe = render_recipe(recipe=new_recipe, template_file="recipe_template.html")
          print(rendered_recipe)
          

Integration with GPT Scripts:

The Helix Framework integrates seamlessly with GPT scripts to leverage the power of language models for recipe generation. GPT scripts can be customized to generate various recipe content, such as introductions, instructions, ingredient lists, and even variations. The framework provides functions for interacting with the OpenAI API and processing responses.

Customization and Extension:

The framework’s modular architecture allows for easy customization and extension. Developers can create their own GPT scripts, templates, and data management strategies to tailor the framework to specific use cases and requirements.

Conclusion:

The Helix Framework provides a comprehensive foundation for recipe generation using GPT models. Its modular design, flexible integration with GPT scripts, and robust data management capabilities make it a powerful tool for automating and enhancing recipe creation processes.