Google Bard has faced escalating competition from ChatGPT. However, with the rollout of their latest Gemini AI models, they aim to reclaim their position in the market. Google has recently revamped Bard AI assistance with Gemini Pro models to enhance user experience, enabling users to input both text and images for accurate and natural responses.
Access and Use Google Gemini API Key for Beginners, This tutorial delves into the Google Gemini API, offering insights into creating advanced AI-driven applications. Leveraging its robust capabilities, you can seamlessly integrate both text and image inputs to produce precise and contextually relevant outputs. The Gemini Python API streamlines integration into your existing projects, facilitating the effortless incorporation of cutting-edge artificial intelligence functionalities.
What is Gemini AI API?
Gemini AI represents a groundbreaking advancement in artificial intelligence (AI), developed collaboratively by teams within Google including Google Research and Google DeepMind. This cutting-edge model is designed to be multimodal, capable of comprehending and processing various types of data such as text, code, audio, images, and videos.
Driven by Google DeepMind’s mission to leverage AI for the greater good, Gemini AI marks significant progress in creating AI models inspired by human perception and interaction with the world. As the most sophisticated and expansive AI model developed by Google to date, Gemini AI boasts high flexibility, enabling it to efficiently operate across a range of systems from data center servers to mobile devices.
Gemini AI is available in three distinct versions, each tailored for specific use cases:
- Gemini Ultra: The pinnacle of sophistication, equipped to handle intricate tasks.
- Gemini Pro: A balanced choice offering robust performance and scalability.
- Gemini Nano: Optimized for mobile devices, prioritizing efficiency.
Among these variants, Gemini Ultra has demonstrated exceptional performance, even surpassing GPT-4 on various metrics. Notably, it is the first model to outperform human experts on the Massive Multitask Language Understanding benchmark, showcasing its superior comprehension and problem-solving abilities.
For those new to Artificial Intelligence, consider exploring the AI Fundamentals skill track, covering essential topics such as ChatGPT, large language models, and generative AI.
Unveiling Google’s Gemini AI Models and API
Google’s Gemini AI models, a recent breakthrough, are the result of collaborative efforts across various teams including Google Research and Google DeepMind. With its unique multimodal design, Gemini possesses the ability to comprehend and interact with a diverse range of data types, including text, code, audio, images, and video.
As Google’s most advanced and extensive AI model to date, Gemini prioritizes adaptability, seamlessly operating across a wide spectrum of systems—from expansive data centers to portable mobile devices. This adaptability holds promise for transforming the landscape for businesses and developers, opening up new possibilities for building and scaling AI applications.
To facilitate exploration and utilization of Gemini’s capabilities, Google has introduced the Gemini API, which is freely accessible. Gemini Ultra, the flagship model, demonstrates state-of-the-art performance, surpassing benchmarks set by GPT-4 on various metrics. Notably, it stands out as the first model to outperform human experts on the Massive Multitask Language Understanding benchmark, showcasing its advanced understanding and problem-solving capabilities.
In a landscape dominated by ChatGPT and OpenAI’s GPT models, Google appeared to recede from the limelight in the AI space. However, the launch of Gemini marked a resurgence, offering foundational models that promise significant advancements. The subsequent introduction of the Gemini API further underscores Google’s commitment to empowering developers. In this guide, we will explore the Gemini API and leverage its capabilities to construct a basic chatbot.
Exploring Advanced Capabilities
To fully leverage the capabilities of the Gemini API, developers may want to explore advanced functionalities such as safety settings and the low-level API.
Safety settings empower users to fine-tune content filtering and permissions within prompts and responses, promoting adherence to community standards. The low-level API offers heightened flexibility for customization, allowing for more granular control over interactions. Multi-turn conversations can be elongated using the GenaiChatSession class, enriching the conversational journey. By delving into these advanced features, developers can craft more sophisticated applications, opening up fresh avenues for user engagement.
Key Highlights Access and Use Google Gemini API Key for Beginners
- Gemini, developed by Google, consists of foundational models tailored for multimodal capabilities, encompassing text, images, audio, and videos. Variants include Gemini Ultra, Gemini Pro, and Gemini Nano, each differing in size and functionalities.
- Gemini has demonstrated superior performance in benchmark assessments, surpassing ChatGPT and GPT4-Vision models across various tests.
- A strong emphasis on responsible AI is evident in Gemini, integrating safety measures to handle unsafe queries by refraining from generating responses and providing safety ratings across different categories.
- Gemini excels in generating multiple candidates for a single prompt, ensuring diverse and contextually relevant responses.
- Gemini Pro features a chat model, enabling developers to create conversational applications effortlessly, with capabilities such as preserving chat history and delivering contextually informed responses.
- Gemini Pro Vision seamlessly handles text and image inputs, allowing it to excel in tasks such as image interpretation and description.
Setting Up a Google Gemini API Key
To begin utilizing the API, the first step is to obtain an API key, which can be acquired from the following link.
How to Access and Use Gemini API for Free:
Once you have obtained the API key, click on the “Get an API key” button and proceed to “Create API key in a new project.” After copying the API key, establish it as an environment variable. For users utilizing Deepnote, this process is simplified. Simply navigate to the integration settings, scroll down, and select the environment variables section. This ensures the proper setup for accessing and utilizing the Gemini API, facilitating a seamless and efficient integration process.
Setting Up Python and Pip on Your Computer
Visit our guide and install Python along with Pip on your PC or Mac. Ensure that you install Python version 3.9 or above. For Linux users, follow our tutorial to install Python and Pip on Ubuntu or other distributions. You can verify the installation of Python and Pip on your computer by running the following commands in the Terminal. They should return the version numbers.
python -V pip -V
After successfully installing Python and Pip, execute the following command to install Google’s Generative AI dependency.
pip install -q -U google-generativeai
Obtaining the Gemini Pro API Key
To acquire the Gemini Pro API key, follow these steps:
Visit makersuite.google.com/app/apikey and sign in with your Google account. Click the “Create API key in new project” button under API keys.
Copy the API key and ensure its confidentiality. Avoid publishing or sharing the API key publicly.
Using the Gemini Pro API Key (Text-only Model)
Google has streamlined the process of utilizing its Gemini API key for development and testing, much like OpenAI. I’ve simplified the code to make it easy for users to test and utilize. In this example, I illustrate how to access the Gemini Pro Text model via the API key.
Open a code editor of your choice. For beginners, consider installing Notepad++. Advanced users may prefer Visual Studio Code.
Copy the code provided below and paste it into your chosen code editor.
import google.generativeai as genai
genai.configure(api_key='PASTE YOUR API KEY HERE')
model = genai.GenerativeModel('gemini-pro')
response = model.generate_content("What is the meaning of life?")
print(response.text)
Once in the code editor, paste your Gemini API key. We’ve defined the ‘gemini-pro’ model, specifically a text-only model. Additionally, we’ve included a query section where you can input questions.
After pasting the API key and defining the model and query, save the code and provide a name for the file. Ensure to append “.py” at the end. For example, I’ve named my file “gemini.py” and saved it on the Desktop.
Next, open the Terminal and execute the following command to navigate to the Desktop.
cd Desktop
After navigating to the Desktop in the Terminal, simply execute the following command to run the gemini.py file using Python.
python gemini.py
Now, the gemini.py file will provide an answer to the question you specified within the code.
To obtain a new response, simply modify the question in the code editor, save the changes, and rerun the gemini.py file. This process allows you to receive a fresh response directly in the Terminal. This demonstrates how you can leverage the Google Gemini API key to access the text-only Gemini Pro model.
Utilizing the Gemini Pro API Key (Text-and-Vision Model)
In this example, I’ll demonstrate how to interact with the Gemini Pro multimodal model. While it’s not yet live on Google Bard, you can access it immediately through the API. Fortunately, the process remains straightforward and seamless.
Open a new file in your preferred code editor and paste the provided code below.
import google.generativeai as genai
import PIL.Image
img = PIL.Image.open('image.jpg')
genai.configure(api_key='PASTE YOUR API KEY HERE')
model = genai.GenerativeModel('gemini-pro-vision')
response = model.generate_content(["what is the total calorie count?", img])
print(response.text)
Ensure to paste your Gemini API key. Here, we’re utilizing the gemini-pro-vision model, which encompasses both text and vision capabilities.
Save the file on your Desktop and append “.py” at the end of the filename. I’ve named it geminiv.py for this example.
In the third line of the code, specify the path to an image.jpg file located on your Desktop. Ensure the image you wish to process is saved in the same location as the geminiv.py file, with the correct filename and extension. You can pass local JPG and PNG files up to 4MB.
In the sixth line of code, input questions related to the image. For instance, if you’re providing a food-related image, you might ask Gemini Pro to calculate the total calorie count.
Now, it’s time to execute the code in the Terminal. Simply navigate to the Desktop (or your specified location) and run the following commands one by one. Remember to save the file if you’ve made any changes.
cd Desktop python geminiv.py
The visual Gemini Pro model provides direct answers to your questions. You can also inquire further and ask the AI to explain its reasoning.
If you want to analyze a different image, ensure that the image filename matches the one specified in the code, update the question accordingly, and rerun the geminiv.py file to receive new responses.
Using the Gemini Pro API Key for Chatting
Thanks to unconv’s concise code on GitHub, you can now engage in a conversation with the Gemini Pro model directly within the Terminal window, utilizing a Gemini AI API key. This eliminates the need to modify the question in the code and rerun the Python file for a new output. You can seamlessly continue the chat within the Terminal window itself.
Moreover, Google has implemented chat history natively, eliminating the need to manually manage conversation history in an array or a list. With a simple function, Google stores all conversation history in a chat session. Here’s how it works:
Open your preferred code editor and paste the provided code below.
import google.generativeai as genai
genai.configure(api_key='PASTE YOUR API KEY HERE')
model = genai.GenerativeModel('gemini-pro')
chat = model.start_chat()
while True:
message = input("You: ")
response = chat.send_message(message)
print("Gemini: " + response.text)
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