rjohnpaul / anyai

AnyAI is a streamlit powered versatile AI tool that integrates Google Gemini and Google Flash API

AnyAI 🚀🤖

AnyAI is a versatile AI tool that integrates Google Gemini and Google Vision APIs to provide powerful content generation and image classification capabilities. This repository contains the source code for the AnyAI web application built using Streamlit.

Features

AI Content Generator

Using the Google Gemini API, AnyAI can generate content for various platforms, including:

• LinkedIn Posts
• Blog Posts
• Instagram Posts
• Tweets
• Product Descriptions
• Emails
• Ad Copies
• Taglines
• Video Scripts
• Podcast Scripts
• SEO Content
• README Files
• Documentation
• Presentations
• Social Media Posts
• Articles

Image Classifier

Using the Google Vision API, AnyAI can classify images either via upload or URL based on the entered query.

Getting Started

Prerequisites

• Python 3.7+
• Streamlit
• Pandas
• Google API credentials

Installation

1. Clone the repository:

   git clone https://github.com/username/AnyAI.git
   cd AnyAI

2. Install the required packages:

   pip install -r requirements.txt

3. Set up your Google API credentials. Create an .env file in the project root and add your API key:

   GOOGLE_API_KEY=your_google_api_key

Running the Application

To run the application, execute the following command in your terminal:

streamlit run app.py

This will start a local server and open the web application in your default web browser.

Usage

1. Visit Google AI Studio: Go to Google AI Studio.

2. Get API Key: Click on "Get API Key" and create your API key.

3. Copy API Key: Copy the generated API key.

4. Paste in Web Application: In your web application, there should be an input field to paste your API key. Paste the copied API key into this field.

5. Generate Content and Classify Images: Once the API key is validated, you can use the Gemini API to generate text content and the Vision API to classify images within your web application.

Gemini Model Capabilities

Capability	Benchmark	Higher is better	Description
MMLU	Representation of questions in 57 subjects	90.0	CoT@32*
CoT@32*	Reasoning	86.4	5-shot** (reported)
Big-Bench Hard	Reading comprehension (F1 Score)	83.6	3-shot
DROP	Commonsense reasoning for everyday tasks	82.4	Variable shots
HellaSwag	Basic arithmetic manipulations	87.8	10-shot*
GSM8K	Basic arithmetic manipulations	94.4	maj1@32
maj1@32	Python code generation	92.0	5-shot CoT (reported)
MATH	Challenging math problems	53.2	4-shot
HumanEval	Python code generation	74.4	0-shot (IT)*
Natural2Code	Python code generation	74.9	0-shot

Learn More

For a deeper understanding and additional insights, check out the accompanying blog post dedicated to Google's Gemini Model. Let's embark on this exciting journey together! 🌟🔍

Contributing

We welcome contributions to AnyAI! Please read our CONTRIBUTING guide for more information on how to contribute.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Acknowledgements

• Google Gemini
• Google Vision
• Streamlit

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Made with ❤️ by John Paul