gemini-gradio 0.0.1

A Python package for creating Gradio applications with Google Gemini models

gemini-gradio

is a Python package that makes it very easy for developers to create machine learning apps that are powered by Google's Gemini API.

Installation

1. Clone the repository:

git clone https://github.com/AK391/gemini-gradio.git

2. Navigate to the project directory:

cd gemini-gradio

3. Install the package in editable mode:

pip install -e .

Basic Usage

You'll need to set up your Gemini API key first:

export GEMINI_API_KEY=<your token>

Then in a Python file, write:

import gradio as gr
import gemini_gradio

gr.load(
    name='gemini-1.5-pro-002',
    src=gemini_gradio.registry,
).launch()

Run the Python file, and you should see a Gradio Interface connected to the Gemini model!

Customization

Once you can create a Gradio UI from a Gemini endpoint, you can customize it by setting your own input and output components, or any other arguments to gr.Interface. For example, the screenshot below was generated with:

import gradio as gr
import gemini_gradio

gr.load(
    name='gemini-1.5-pro-002',
    src=gemini_gradio.registry,
    title='Gemini-Gradio Integration',
    description="Chat with Gemini Pro model.",
    examples=["Explain quantum gravity to a 5-year old.", "How many R are there in the word Strawberry?"]
).launch()

Composition

Or use your loaded Interface within larger Gradio Web UIs, e.g.

import gradio as gr
import gemini_gradio

with gr.Blocks() as demo:
    with gr.Tab("Gemini Pro"):
        gr.load('gemini-1.5-pro-002', src=gemini_gradio.registry)
    with gr.Tab("gemini-1.5-flash"):
        gr.load('gemini-1.5-flash', src=gemini_gradio.registry)

demo.launch()

Under the Hood

The gemini-gradio Python library has two dependencies: google-generativeai and gradio. It defines a "registry" function gemini_gradio.registry, which takes in a model name and returns a Gradio app.

Supported Models in Gemini

All chat API models supported by Google's Gemini are compatible with this integration. For a comprehensive list of available models and their specifications, please refer to the Google AI Studio documentation.

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Note: if you are getting an authentication error, then the Gemini API Client is not able to get the API token from the environment variable. You can set it in your Python session like this:

import os

os.environ["GEMINI_API_KEY"] = ...