Gemini API and Flutter: Practical, AI-driven apps with Google AI tools
Science & Technology
Introduction
Large Language Models (LLMs), like Google's Gemini, are sophisticated artificial intelligence systems trained on extensive datasets. These generative AI models can create various forms of content, including text, images, code, and music. The rise of generative AI has the potential to revolutionize how developers interact with applications. For those in the development community, staying updated with the latest AI tools and understanding their practical applications can be a challenge.
As engineers and product managers on the Dart and Flutter teams, we aimed to explore how generative AI could enhance app development. Our journey led us to create a cooking app that leverages the Gemini API through Google AI tools.
Getting Started with Generative AI
Our initial step was to familiarize ourselves with generative AI, which we did using Google AI Studio. Google AI Studio is a web-based IDE designed for prototyping with Google’s generative models. It provides a valuable platform for experimenting with different prompts while building features that utilize the Gemini API.
Through our exploration, we recognized the issues traditional cooking apps often face, such as requiring users to input their pantry items manually and relying on large databases of recipes. We developed the idea that using a photo of available ingredients could streamline the process, allowing users to generate recipes without requiring previous knowledge of food items.
Proof of Concept: Prompt Design
To prove the feasibility of our app concept, we employed a process called prompt design. This involves creating and refining prompts given to large language models to yield desired outputs. Our first task was to determine which type of prompt suited our needs best: free-form, structured, or chat-based.
We began experimenting with a free-form prompt in Google AI Studio using the Gemini 1.5 Pro model. Initially, we entered a general query asking for recipes based on a photo. After several iterations, we found that the free-form prompt provided just the right balance of flexibility and quality for our application.
We then refined the prompts to include specifics, such as the number of servings and nutritional information. Additionally, we implemented safety protocols by instructing the model to ensure it only generated recipes from edible items while considering food safety guidelines.
Building the Flutter App
Once we established a functional prompt, we moved on to integrate this functionality within a Flutter app. When users open this cooking app, they’re greeted by Chef Noodle, who requests photos of the ingredients they plan to use. The users can enhance their recipe requests by adding dietary restrictions and preferred cuisines.
The app interprets users' inputs and sends a request to the Gemini API, generating a relevant recipe. For effective communication with the API, we outlined necessary steps, including acquiring an API key, adding the Google generative AI package to our Flutter project, and writing the logic for making requests with the designed prompt.
As we progressed, we added personality to Chef Noodle, enriching the user experience by having the chef offer interesting tidbits when providing recipes.
Structuring Data for Reliability
Initially, the Gemini API returned recipe information as markdown, which was manageable but soon became complex as we requested more detailed information. We realized that we needed to specify the expected data types in our prompts to ensure reliable responses. After implementing this change, we saw a significant improvement in the consistency of the data returned, making it easier to process within our app.
Conclusion
The culmination of our efforts resulted in an efficient cooking app powered by the Gemini API. This solution not only eliminated the need for a comprehensive recipe database but also simplified the user's journey in finding a recipe by taking a picture of ingredients. By leveraging the capabilities of Flutter and Gemini, we streamlined the app development process and created a functional tool that offers a unique user experience.
In the future, we plan to enhance the interactivity of the app by integrating chat features using the Gemini API. For those interested in similar endeavors, resources, including a GitHub repository, are available in the video's description. By exploring the potential of AI-driven applications with the Gemini API, we look forward to seeing the innovative solutions you can create.
Keywords
- Gemini API
- Generative AI
- Flutter
- AI-driven apps
- Prompt design
- Cooking app
- UI/UX
- Google AI Studio
FAQ
Q: What is the Gemini API?
A: The Gemini API is a generative AI tool developed by Google that can produce content such as text and recipes based on prompts input by users.
Q: How does prompt design work?
A: Prompt design involves creating and tuning prompts given to an LLM like Gemini to obtain desired outputs. It can vary from free-form to structured or chat-based prompts.
Q: What problems does the cooking app aim to solve?
A: The cooking app aims to simplify recipe discovery by allowing users to take photos of ingredients instead of manually entering them, thus eliminating the need for a pre-existing database of recipes.
Q: How can I get started using the Gemini API?
A: You can begin using the Gemini API by acquiring an API key through Google AI Studio, adding the necessary AI packages to your Flutter project, and referencing the documentation for integration steps.
Q: What are future plans for the cooking app?
A: Future enhancements include integrating chat features to make the app more interactive and enriching the user experience further.
