# Copyright 2025 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# SPDX-License-Identifier: Apache-2.0
"""This sample demonstrates how to use Gemini to describe and draw images."""
import asyncio
import base64
import os
from io import BytesIO
from PIL import Image
from genkit.ai import Genkit
from genkit.plugins.google_genai import GoogleAI, googleai_name
from genkit.types import Media, MediaPart, Message, Role, TextPart
ai = Genkit(plugins=[GoogleAI()])
@ai.flow()
async def draw_image_with_gemini() -> str:
"""Draw an image.
Returns:
The image.
"""
return await ai.generate(
prompt='Draw a cat in a hat.',
config={'response_modalities': ['Text', 'Image']},
model=googleai_name('gemini-2.5-flash-image'),
)
@ai.flow()
async def describe_image_with_gemini(data: str) -> str:
"""Describe an image.
Args:
data: The image data as a data URI (e.g., 'data:image/jpeg;base64,...').
Returns:
The description of the image.
"""
if not (data.startswith('data:') and ',' in data):
raise ValueError(f'Expected a data URI (e.g., "data:image/jpeg;base64,..."), but got: {data[:50]}...')
result = await ai.generate(
messages=[
Message(
role=Role.USER,
content=[
TextPart(text='What is shown in this image?'),
MediaPart(media=Media(content_type='image/jpeg', url=data)),
],
),
],
model=googleai_name('gemini-3-flash-preview'),
)
return result.text
async def main() -> None:
"""Main function."""
# Gemini describes an image. Works both on Gemini API and VertexAI API.
# Make sure that there is image.jpg on root directory of current sample.
current_dir = os.path.dirname(os.path.abspath(__file__))
image_path = os.path.join(current_dir, '..', 'image.jpg')
with open(image_path, 'rb') as image_file:
buffer = image_file.read()
img_base64 = base64.b64encode(buffer).decode('utf-8')
data_uri = f'data:image/jpeg;base64,{img_base64}'
print(await describe_image_with_gemini(data_uri))
# Gemini draws an image by description. The model used is available only in
# Gemini API.
result = await draw_image_with_gemini()
# Find the media part in the response
media_part = next((part.root.media for part in result.message.content if part.root.media is not None), None)
if media_part is None:
print('No media found in response')
print(f'Response content: {result.message.content}')
return
media_url = media_part.url
# Extract base64 data after the comma in "data:image/png;base64,..."
base64_data = media_url.split(',', 1)[1]
decoded_image = BytesIO(base64.b64decode(base64_data))
image = Image.open(decoded_image)
image.show('Image generated by Gemini')
if __name__ == '__main__':
ai.run_main(main())
