bananaproai-com

A helpful Elixir library for interacting with the Banana Pro AI inference platform. This package provides a streamlined interface for building and deploying machine learning models with ease.

Installation

To install bananaproai-com, add it as a dependency to your mix.exs file: elixir def deps do [

{:bananaproai_com, "~> 0.1.0"} # Replace with the latest version

] end

Then, run mix deps.get to fetch the dependency.

Usage

Before using the library, configure your API key. You can set it via application configuration: elixir config :bananaproai_com, api_key: "YOUR_API_KEY"

Or, set it as an environment variable BANANA_API_KEY.

Here are some examples of how to use the bananaproai-com library:

1. Invoking a model with input data: elixir alias BananaProAI.Client

input_data = %{ "prompt" => "Generate a picture of a cat wearing a hat" }

case Client.call("your-model-id", input_data) do {:ok, response} ->

IO.inspect(response) # Inspect the API response

->

IO.puts("Error: #{error}")

end

2. Handling different response types: elixir alias BananaProAI.Client

input_data = %{ "text" => "Translate to French: Hello world!" }

case Client.call("translation-model", input_data) do {:ok, %{"text" => translated_text}} ->

IO.puts("Translation: #{translated_text}")

->

IO.puts("Unexpected response: #{inspect(response)}")

->

IO.puts("Error: #{error}")

end

3. Using with for cleaner error handling: elixir alias BananaProAI.Client

input_data = %{ "image_url" => "https://example.com/image.jpg" }

with {:ok, response} <- Client.call("image-processing-model", input_data),

 {:ok, image_data} <- Map.fetch(response, "processed_image") do

IO.puts("Processed image data: #{image_data}") else {:error, error} ->

IO.puts("Error: #{error}")

_ ->

IO.puts("Unexpected error occurred.")

end

4. Passing parameters and processing results within a pipe: elixir alias BananaProAI.Client

input_data = %{ "input_string" => "This is a test." }

input_data |> Client.call("string-analysis-model") |> case do {:ok, %{"sentiment" => sentiment}} ->

IO.puts("Sentiment: #{sentiment}")

->

IO.puts("Unexpected response: #{inspect(response)}")

->

IO.puts("Error: #{error}")

end

5. Handling asynchronous calls (if the API supports it): elixir alias BananaProAI.Client

input_data = %{ "complex_task" => "Do something computationally intensive" }

case Client.call("long-running-model", input_data, async: true) do {:ok, %{"task_id" => task_id}} ->

IO.puts("Task submitted. Task ID: #{task_id}")
# Poll the API later using the task_id to get the result

->

IO.puts("Error: #{error}")

end

Features

• Easy integration with the Banana Pro AI platform.
• Simple function calls to run your models.
• Robust error handling.
• Support for synchronous and asynchronous calls.
• Flexible input and output data structures.

License

MIT

This package is part of the bananaproai-com ecosystem. For advanced features and enterprise-grade tools, visit: https://bananaproai.com/