Disclaimer

This is a research project. Please do not use it commercially and use it responsibly.

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WebAI-to-API

WebAI-to-API is a modular web server built with FastAPI, designed to manage requests across AI services like Gemini. It features a clean, extendable architecture that simplifies configuration, integration, and maintenance.

Note: Currently, Gemini is the primary supported AI service.

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Features

• 🌐 Endpoints Management:
  • /v1/chat/completions
  • /gemini
  • /gemini-chat
  • /translate
• 🔄 Service Switching: Easily configure and switch between AI providers via config.conf.
• 🛠️ Modular Architecture: Organized into clearly defined modules for API routes, services, configurations, and utilities, making development and maintenance straightforward.

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Installation

1. Clone the repository:

   git clone https://github.com/Amm1rr/WebAI-to-API.git
   cd WebAI-to-API

2. Install dependencies using Poetry:

   poetry install

3. Create and update the configuration file:

   cp config.conf.example config.conf

   Then, edit config.conf to adjust service settings and other options.

4. Run the server:

   poetry run python src/run.py

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Usage

Send a POST request to /v1/chat/completions (or any other available endpoint) with the required payload.

Example Request

{
  "model": "gemini-2.0-flash",
  "messages": [{ "role": "user", "content": "Hello!" }]
}

Example Response

{
  "id": "chatcmpl-12345",
  "object": "chat.completion",
  "created": 1693417200,
  "model": "gemini-2.0-flash",
  "choices": [
    {
      "message": {
        "role": "assistant",
        "content": "Hi there!"
      },
      "finish_reason": "stop",
      "index": 0
    }
  ],
  "usage": {
    "prompt_tokens": 0,
    "completion_tokens": 0,
    "total_tokens": 0
  }
}

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Roadmap

• ✅ Gemini Support: Implemented
• 🟡 Claude, ChatGPT Development: Discontinued

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Configuration ⚙️

Key Configuration Options

Section	Option	Description	Example Value
[AI]	default_ai	Default service for /v1/chat/completions	gemini
[EnabledAI]	gemini	Enable/disable Gemini service	true
[Browser]	name	Browser for cookie-based authentication	firefox

The complete configuration template is available in WebAI-to-API/config.conf.example.
If the cookies are left empty, the application will automatically retrieve them using the default browser specified.

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Sample config.conf

[AI]
# Default AI service.
default_ai = gemini

# Default model for Gemini.
default_model_gemini = gemini-2.0-flash

# Gemini cookies (leave empty to use browser_cookies3 for automatic authentication).
gemini_cookie_1psid =
gemini_cookie_1psidts =

[EnabledAI]
# Enable or disable AI services.
gemini = true

[Browser]
# Default browser options: firefox, brave, chrome, edge, safari.
name = firefox

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Project Structure

The project now follows a modular layout that separates configuration, business logic, API endpoints, and utilities:

src/
├── app/
│   ├── __init__.py
│   ├── main.py                # FastAPI app creation, configuration, and lifespan management.
│   ├── config.py              # Global configuration loader/updater.
│   ├── logger.py              # Centralized logging configuration.
│   ├── endpoints/             # API endpoint routers.
│   │   ├── __init__.py
│   │   ├── gemini.py          # Endpoints for Gemini (e.g., /gemini, /gemini-chat).
│   │   └── chat.py            # Endpoints for translation and OpenAI-compatible requests.
│   ├── services/              # Business logic and service wrappers.
│   │   ├── __init__.py
│   │   ├── gemini_client.py   # Gemini client initialization, content generation, and cleanup.
│   │   └── session_manager.py # Session management for chat and translation.
│   └── utils/                 # Helper functions.
│       ├── __init__.py
│       └── browser.py         # Browser-based cookie retrieval.
├── models/                    # Models and wrappers (e.g., MyGeminiClient).
│   └── gemini.py
├── schemas/                   # Pydantic schemas for request/response validation.
│   └── request.py
├── config.conf                # Application configuration file.
└── run.py                     # Entry point to run the server.

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Developer Documentation

Overview

The project is built on a modular architecture designed for scalability and ease of maintenance. Its primary components are:

• app/main.py: Initializes the FastAPI application, configures middleware, and manages application lifespan (startup and shutdown routines).
• app/config.py: Handles the loading and updating of configuration settings from config.conf.
• app/logger.py: Sets up a centralized logging system.
• app/endpoints/: Contains separate modules for handling API endpoints. Each module (e.g., gemini.py and chat.py) manages routes specific to their functionality.
• app/services/: Encapsulates business logic, including the Gemini client wrapper (gemini_client.py) and session management (session_manager.py).
• app/utils/browser.py: Provides helper functions, such as retrieving cookies from the browser for authentication.
• models/: Holds model definitions like MyGeminiClient for interfacing with the Gemini Web API.
• schemas/: Defines Pydantic models for validating API requests.

How It Works

1. Application Initialization:
   On startup, the application loads configurations and initializes the Gemini client and session managers. This is managed via the lifespan context in app/main.py.

2. Routing:
   The API endpoints are organized into dedicated routers under app/endpoints/, which are then included in the main FastAPI application.

3. Service Layer:
   The app/services/ directory contains the logic for interacting with the Gemini API and managing user sessions, ensuring that the API routes remain clean and focused on request handling.

4. Utilities and Configurations:
   Helper functions and configuration logic are kept separate to maintain clarity and ease of updates.

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🐳 Docker Deployment Guide

For Docker setup and deployment instructions, please refer to the Docker.md documentation.

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Star History

License 📜

This project is open source under the MIT License.

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Note: This is a research project. Please use it responsibly, and be aware that additional security measures and error handling are necessary for production deployments.