gemini-to-open-api-wrapper

Set up the API Environment: You will need Python and the Flask framework to create a local API server. You'll also need the Google AI for Developers library to interact with the Gemini API[3].

• Install Flask and the Google AI Python library:

  pip install Flask google-generativeai
  

API Key Setup: Get a Gemini API key from Google AI for Developers[3].

Code the Translation Layer: Create a Flask application that listens for OpenAI-compatible requests and translates them to Gemini API requests[4].

• Here’s a basic example of a Flask application (app.py) that does this:

  from flask import Flask, request, jsonify
  import google.generativeai as genai
  
  app = Flask(__name__)
  
  # Replace with your actual Gemini API key
  GOOGLE_API_KEY = "YOUR_GEMINI_API_KEY"
  genai.configure(api_key=GOOGLE_API_KEY)
  
  # Function to translate OpenAI-like request to Gemini
  def translate_to_gemini(openai_request):
      messages = openai_request.get('messages', [])
  
      # Extract the user message
      user_message = next((msg['content'] for msg in messages if msg['role'] == 'user'), None)
  
      return user_message
  
  # Function to call Gemini API
  def call_gemini_api(prompt):
      model = genai.GenerativeModel('gemini-2.0-flash')
      try:
          response = model.generate_content(prompt)
          return response.text
      except Exception as e:
          return str(e)
  
  @app.route('/v1/chat/completions', methods=['POST'])
  def chat_completions():
      data = request.get_json()
  
      # Translate the OpenAI-like request to Gemini
      gemini_prompt = translate_to_gemini(data)
  
      # Call the Gemini API
      gemini_response = call_gemini_api(gemini_prompt)
  
      # Format the response to be OpenAI-compatible
      response = {
          "choices": [
              {
                  "message": {
                      "role": "assistant",
                      "content": gemini_response
                  }
              }
          ]
      }
  
      return jsonify(response)
  
  if __name__ == '__main__':
      app.run(debug=True, port=5000)
  

Run the application: Execute the Python script to start the local server[3].

python app.py

This starts a local server on port 5000.

Configure aichat: Point aichat to your local server by changing the base URL to http://localhost:5000/v1[4]. You may need to configure the tool to send requests in the OpenAI format, which your Flask app is designed to handle.

Testing: Send a request to http://localhost:5000/v1/chat/completions with a payload that mimics the OpenAI format to test if the translation works correctly[4].

• Example payload:

  {
      "model": "gemini-2.0-flash",
      "messages": [
          {"role": "user", "content": "Explain how AI works"}
      ]
  }
  

Deployment Notes: Consider using environment variables for sensitive information like API keys. For production, use a more robust WSGI server like Gunicorn or uWSGI instead of the Flask development server[5].