Gemini API

Abstract

1. Introduction

A reverse-engineered asynchronous Python wrapper for the Google Gemini web app (formerly Bard).

2. Objective

✨ Reverse-engineered Python API for Google Gemini web app

This project demonstrates how Python can be applied to a real-world AI & Machine Learning problem.

3. Key Features / Modules

• Persistent Cookies - Automatically refreshes cookies in background. Optimized for always-on services.
• Image Generation - Natively supports generating and editing images with natural language.
• Video & Audio Generation - Supports generating videos and audio/music content natively.
• Deep Research - Full deep research workflow with plan creation, status polling, and result retrieval.
• System Prompt - Supports customizing the model's system prompt with Gemini Gems.
• Extension Support - Supports generating content with Gemini extensions, such as YouTube and Gmail.
• Classified Outputs - Categorizes text, thoughts, images, videos, and audio in the response.
• Streaming Mode - Supports stream generation, yielding partial outputs as they are generated.
• CLI Tool - Standalone command-line interface for quick interactions.
• Official Flavor - Provides a simple and elegant interface inspired by Google Generative AI's official API.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/HanaokaYuzu/Gemini-API.git
cd Gemini-API

pip install -U gemini_webapi

pip install -U gemini_webapi[browser]

{ "__Secure-1PSID": "value...", "__Secure-1PSIDTS": "value..." }

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Deploy the model as a web app with Streamlit, Flask or FastAPI
• Compare against an additional model and report the metric difference
• Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What dataset does the project use and how was it pre-processed?
2. Which algorithm / model architecture is used and why was it chosen over alternatives?
3. How are training and testing data split, and how is overfitting avoided?
4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by HanaokaYuzu and published on GitHub under the GNU Affero General Public License v3.0. Please follow the license terms and credit the original author when you use or modify this code.