Google Genai Starter Template

A comprehensive FastAPI starter template for working with Google's Gemini AI models, supporting text, image, and audio processing.

Features

• Text Processing

  • Text generation with prompts
  • Structured chat conversations
  • Context-aware text generation

• Image Processing

  • Single image analysis
  • Multiple image analysis
  • Image comparison

• Audio Processing

  • Audio transcription
  • Content analysis
  • Audio summarization

Installation

1. Clone the repository:

git clone https://github.com/yourusername/google-genai-starter-template.git
cd google-genai-starter-template

2. Install dependencies:

pip install -r requirements.txt

3. Install additional audio dependencies:

# For Ubuntu/Debian
sudo apt-get install ffmpeg

# For macOS
brew install ffmpeg

# For Windows
# Download ffmpeg from https://ffmpeg.org/download.html

4. Set up environment variables:

export GOOGLE_API_KEY="your_api_key_here"

Usage

1. Start the server:

uvicorn app.main:app --reload

2. Access the API documentation:

• Swagger UI: http://localhost:8000/docs
• ReDoc: http://localhost:8000/redoc

API Endpoints

Text Processing

POST /api/text/generate
POST /api/text/chat
POST /api/text/context

Image Processing

POST /api/image/analyze
POST /api/image/analyze-multiple
POST /api/image/compare

Audio Processing

POST /api/audio/transcribe
POST /api/audio/analyze
POST /api/audio/summarize

Example Requests

Text Generation

import requests

response = requests.post(
    "http://localhost:8000/api/text/generate",
    json={
        "prompt": "Write a short story about AI",
        "temperature": 0.7
    }
)
print(response.json())

Image Analysis

import requests

files = {
    'image': open('image.jpg', 'rb'),
    'prompt': (None, 'Describe this image'),
    'temperature': (None, '0.7')
}

response = requests.post(
    "http://localhost:8000/api/image/analyze",
    files=files
)
print(response.json())

Audio Processing

import requests

files = {
    'audio_file': open('audio.mp3', 'rb'),
    'language': (None, 'en-US')
}

response = requests.post(
    "http://localhost:8000/api/audio/transcribe",
    files=files
)
print(response.json())

Dependencies

• FastAPI
• google-generativeai
• Pillow
• SpeechRecognition
• pydub
• python-multipart
• uvicorn

Development

1. Install development dependencies:

pip install pytest black isort flake8

2. Run tests:

pytest

3. Format code:

black .
isort .

Contributing

1. Fork the repository
2. Create your feature branch
3. Commit your changes
4. Push to the branch
5. Create a new Pull Request

License

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

Support

For support, please open an issue in the GitHub repository.