Environmental sustainability planning

TEXT_INPUT:Ask the user to enter their sustainability goal;;CHATGPT:Ask ChatGPT for tips and suggestions on achieving the goal;;HTTP_REQUEST:Fetch local recycling centers near the user's location;;DALL_E:Generate an image illustrating the sustainability goal;;DISPLAY_OUTPUT:Display the tips, recycling centers, and the generated image

Generative AI models like Language Models (LLMs) and Image Generators can effectively contribute towards environmental sustainability planning. For example, ChatGPT can generate insightful reports on eco-friendly initiatives, predict their potential impacts, and propose improvements for sustainability strategies. It can analyze large datasets of environmental data and provide human-like summaries and recommendations.

Further, AI image generators can be trained on large datasets of satellite images to predict future environmental conditions, such as flood patterns or deforestation. They can generate images depicting various environmental conditions, providing valuable visualizations that aid in planning ecological initiatives. Additionally, these AI tools can aid in creating simulation models of new sustainably-designed urban infrastructures or green spaces, giving stakeholders a clear vision of proposed projects.

Altogether, these AI models can help optimize environmental conservation efforts, inform policy decisions, and encourage sustainable behavior.

How to build with Clevis

This is an example of an application you can build using Clevis, an AI tool, called the 'Environmental Sustainability Planner'. This application helps users plan and maintain their environmental sustainability efforts, showing the power and possibilities Clevis has within the sustainability realm.

The application starts with the Text Input step, where it prompts users to input their sustainability goal. It then moves to the ChatGPT step, employing OpenAI's language model, ChatGPT, to generate unique tips and suggestions on how to achieve the mentioned goal. This inquiry to ChatGPT is dynamic and tailored to the user's input.

Next, the Http Request step comes into play. Here, the app sends a request to a hypothetical API, fetching local recycling centers based upon the user's location. This data is then returned in the response.

Using OpenAI's DALL-E, the application generates an image representing the sustainability goal in the DALL·E step. The text input for this image creation comes from the user's initial goal input.

Finally, the Display Output step displays to the user: the tips from ChatGPT, nearby recycling centers, and the generated image of their sustainability goal. This ends a cycle of an always-improving, custom plan for users' environmental sustainability efforts, delivering efficient, realistic, and visually engaging results.