[PAID] :google:emini Extension to interact with the Gemini-pro and PaLM 2 models from :google:oogle

Gemini

The Gemini extension for :kodular:odular allows you to interact with the Google :google:emini-Pro and :google:emini-Pro-Vesion models and these models that Bard is based on to generate text and control a stream of text generation.

Features of the Gemini extension for kodular :

  • Generate text using the Google Gemini API.

  • Stream generated text in real time.

  • Generate text with images.

  • Encode images to Base64 with the -w0 option.

  • Handle errors gracefully.

Benefits of using the Gemini extension:

  • Easy to use: The Gemini extension provides a simple and easy-to-use interface for interacting with the Gemini API.

  • Powerful: The Gemini API is a powerful tool for generating text, and the Gemini extension makes it easy to use this API in your Kodular apps.

  • Versatile: The Gemini extension can be used for a variety of tasks, such as generating customer support responses, writing creative content, and translating languages.

  • Extensible: The Gemini extension is extensible, and new features can be added in the future.

Here are some specific examples of how the Gemini extension can be used:

  • Create a chatbot that can respond to user queries in a natural and engaging way.

  • Generate marketing copy for your products or services.

  • Write blog posts and articles on a variety of topics.

  • Translate languages in real time.

  • Create interactive stories and games.

The possibilities are endless!

Explanation

Generating Content

Screenshot 2023-12-16 235605

To generate content using Gemini, you can use the GenerateGeminiContent block. This block takes two arguments:

  • contents: A list of dictionaries, where each dictionary represents a content item. Each content item can have ,
    So the JSON input for contents will be like this
[
        {"role":"user",
        "parts":[{
          "text": "Write the first line of a story about a magic backpack."}]},
        {"role": "model",
         "parts":[{
           "text": "In the bustling city of Meadow brook, lived a young girl named Sophie. She was a bright and curious soul with an imaginative mind."}]},
        {"role": "user",
         "parts":[{
           "text": "Can you set it in a quiet village in 1600s France?"}]},
      ]

Blocks example:

the following keys:
* role: A string representing the role of the content item in the conversation.
* parts: A list of dictionaries, where each dictionary represents a part of the content item. Each part can have the following keys:
* text: A string representing the text of the part.

Screenshot 2023-12-16 235605

The GenerateGeminiContent block will generate content using the specified parameters and return the result in the RespondedToGemini event.

The RespondedToGemini event will be triggered with the following parameters:

  • apiResponse: The raw API response from Gemini.
  • textParts: A list of strings representing the generated text parts.
  • role: The role of the generated content.
  • finishReason: The reason why the generation was finished.
  • index: The index of the generated content.
  • safetyRatings: A list of dictionaries representing the safety ratings of the generated content. Each dictionary will have the following keys:
    • category: The category of the safety rating.
    • probability: The probability of the safety rating.

Streaming Content

component_method

To generate content in a stream, you can use the StreamGenerateGeminiContent block. This block takes two arguments:

  • contents: A list of dictionaries, where each dictionary represents a content item. Each content item can have the following keys:
    • role: A string representing the role of the content item in the conversation.
    • parts: A list of dictionaries, where each dictionary represents a part of the content item. Each part can have the following keys:
      • text: A string representing the text of the part. so the JSON input for contents will be like this
[
        {"role":"user",
         "parts":[{
           "text": "Write the first line of a story about a magic backpack."}]},
        {"role": "model",
         "parts":[{
           "text": "In the bustling city of Meadow brook, lived a young girl named Sophie. She was a bright and curious soul with an imaginative mind."}]},
        {"role": "user",
         "parts":[{
           "text": "Can you set it in a quiet village in 1600s France?"}]},
      ]

Blocks examble

  • api key: Your Google Cloud API key.

The StreamGenerateGeminiContent block will open a stream of content generation using the specified parameters. The generated content will be returned in the GotGeminiStream event.

component_event

The GotGeminiStream event will be triggered with the following parameter:

  • text: A string representing the generated text.

blocks

You can manually stop the stream using the StopStream block. The StoppedStream event will be triggered when the stream is stopped.

component_method(2)

You can also check if the stream is currently running using the IsStreaming block.



blocks

Generating Content with Images

To generate content using images, you can use the StreamGenerateGeminiVisionContent block. This block takes two arguments:

  • contents: A list of dictionaries, where each dictionary represents a content item. Each content item can have the following keys:
    • role: A string representing the role of the content item in the conversation.
    • parts: A list of dictionaries, where each dictionary represents a part of the content item. Each part can have the following keys:
      • text: A string representing the text of the part.
      • inlineData: A dictionary representing inline data, such as an image. The inlineData dictionary can have the following keys:
        • mimeType: The MIME type of the inline data.
        • data: The base64-encoded data of the inline data.
          So the JSON input for contents will be like this
[
       {
         "text": "Describe what the people are doing in this image:\n"
       },
       {
         "inlineData": {
           "mimeType": "image/png",
           "data": "'$(base64 -w0 image0.jpeg)'"
         }
       },
       {
         "text": " "
       }
     ]
   }
 ]

Blocks example :

  • api key: Your Google Cloud API key.

component_event

The StreamGenerateGeminiVisionContent block will open a stream of content generation using the specified parameters. The generated content will be returned in the GotGeminiStream event.



blocks

Encoding Images to Base64

The EncodeImageToBase64 block can be used to encode an image file to Base64 with the -w0 option, which removes all line breaks from the encoded string. This can be useful for sending images to the Gemini API.

The EncodeImageToBase64 block takes one argument:

  • imagePath: The path to the image file.

The EncodeImageToBase64 block will return the base64-encoded image as a string.


Error Handling

blocks

The ErrorOccurred event will be triggered if an error occurs while using the Gemini extension. The event will be triggered with the following parameters:

  • message: A string describing the error.
  • component: The name of the component that caused the error.

Examples

Here is an example of how to use the Gemini extension to generate text:

contents = [{"role": "user", "parts": [{"text": "Hello, Gemini!"}]}]
api_key = "YOUR_API_KEY"
GenerateGeminiContent(contents, api_key)

Bocks:



Here is an example of how to use the Gemini extension to generate text in a stream:

contents = [{"role": "user", "parts": [{"text": "Hello, Gemini!"}]}]
api_key = "YOUR_API_KEY"
StreamGenerateGeminiContent(contents, api_key)

Bocks:

Here is an example of how to use the Gemini extension to generate text with images:


contents = [
  {
    "role": "user",
    "parts": [
      {"text": "Here is an image of a cat:"},
      {"inlineData": {"mimeType": "image/jpeg", "data": base64_image}}
    ]
  }
]

api_key = "YOUR_API_KEY"

Bocks:

Here is an example of how to use the Gemini extension to generate text with images in FreeForm Prompt:

you can use this extension to convert the TextBox component to FreeForm layout :


contents = [
             {
      "parts": [
        {
          "text": "Describe what the people are doing in this image:\n"
        },
        {
          "inlineData": {
            "mimeType": "image/jpeg",
            "data": "'$(base64 -w0 image0.jpeg)'"
          }
        },
        {
          "text": "\nand what is the relation between this is mage to \n"
        },
        {
          "inlineData": {
            "mimeType": "image/webp",
            "data": "'$(base64 -w0 image1.webp)'"
          }
        }
      ]
    }
  ]

api_key = "YOUR_API_KEY"

Bocks:

Freeform preview example:

PaLM_2 blocks palm


blocks(3)

component_event(1)

component_event

PaLM 2
PaLM 2 is a large language model (LLM) developed by Google that can perform various tasks involving natural language understanding and generation, such as reasoning, coding, mathematics, and multilingual translation. It is an improved version of PaLM, which was released in 2022. PaLM 2 is based on three main innovations: compute-optimal scaling, improved dataset mixture, and updated model architecture and objective. PaLM 2 is also used in other generative AI tools, such as the PaLM API and Bard


Applications that use this extension :

video preview:

Aix_file:

Price: $5.99

PayPal payment URL: Purchase Gemini.aix , After payment you will be directed to the download URL so you do not have to contact me to get the extension file however you can contact me in case of any problems

Have Inquiries?

For any queries regarding the Gemini extension, feel free to reach out at PM

Note :

You can try Gemini and get you API key from here

3 Likes

Wow Nice Extension and nice work :two_hearts::sparkling_heart:

1 Like

Can you please get back to me on the features i asked for because i have a deadline and i don’t know if you’re working on it or not, kindly respond asap. Thanks

Thank you very much :dizzy:

check your PM please

PaLM 2 NewBlocks added

1 Like

I purchased the Gemini extension for AI2 from the MIT APP Inventor community, but PayPal did not redirect me to download the file. The purchase was made on May 17th of this year. I have already left some messages in the MIT APP Inventor community. What is the procedure for me to download the file?