transform_image_from_encoded
Modify existing images using text prompts with Google's Gemini AI. Upload encoded images and describe changes to generate transformed versions saved locally.
Instructions
Transform an existing image based on the given text prompt using Google's Gemini model.
Args:
encoded_image: Base64 encoded image data with header. Must be in format:
"data:image/[format];base64,[data]"
Where [format] can be: png, jpeg, jpg, gif, webp, etc.
prompt: Text prompt describing the desired transformation or modifications
Returns:
Path to the transformed image file saved on the server
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| encoded_image | Yes | ||
| prompt | Yes |
Implementation Reference
- src/gemini_image_mcp/server.py:272-301 (handler)Main handler function for transform_image_from_encoded tool, including registration via @mcp.tool(), input schema in docstring, and core logic that loads the base64 image, translates prompt, and processes transformation.@mcp.tool() async def transform_image_from_encoded(encoded_image: str, prompt: str) -> str: """Transform an existing image based on the given text prompt using Google's Gemini model. Args: encoded_image: Base64 encoded image data with header. Must be in format: "data:image/[format];base64,[data]" Where [format] can be: png, jpeg, jpg, gif, webp, etc. prompt: Text prompt describing the desired transformation or modifications Returns: Path to the transformed image file saved on the server """ try: logger.info(f"Processing transform_image_from_encoded request with prompt: {prompt}") # Load and validate the image source_image, _ = await load_image_from_base64(encoded_image) # Translate the prompt to English translated_prompt = await translate_prompt(prompt) # Process the transformation return await process_image_transform(source_image, translated_prompt, prompt) except Exception as e: error_msg = f"Error transforming image: {str(e)}" logger.error(error_msg) return error_msg
- Helper function to load and validate the base64-encoded image into a PIL Image object, specifically used by the transform_image_from_encoded handler.async def load_image_from_base64(encoded_image: str) -> Tuple[PIL.Image.Image, str]: """Load an image from a base64-encoded string. Args: encoded_image: Base64 encoded image data with header Returns: Tuple containing the PIL Image object and the image format """ if not encoded_image.startswith('data:image/'): raise ValueError("Invalid image format. Expected data:image/[format];base64,[data]") try: # Extract the base64 data from the data URL image_format, image_data = encoded_image.split(';base64,') image_format = image_format.replace('data:', '') # Get the MIME type e.g., "image/png" image_bytes = base64.b64decode(image_data) source_image = PIL.Image.open(BytesIO(image_bytes)) logger.info(f"Successfully loaded image with format: {image_format}") return source_image, image_format except ValueError as e: logger.error(f"Error: Invalid image data format: {str(e)}") raise ValueError("Invalid image data format. Image must be in format 'data:image/[format];base64,[data]'") except base64.binascii.Error as e: logger.error(f"Error: Invalid base64 encoding: {str(e)}") raise ValueError("Invalid base64 encoding. Please provide a valid base64 encoded image.") except PIL.UnidentifiedImageError: logger.error("Error: Could not identify image format") raise ValueError("Could not identify image format. Supported formats include PNG, JPEG, GIF, WebP.") except Exception as e: logger.error(f"Error: Could not load image: {str(e)}") raise
- Helper function that creates the transformation prompt and calls Gemini to process the image transformation, used by the handler.async def process_image_transform( source_image: PIL.Image.Image, optimized_edit_prompt: str, original_edit_prompt: str ) -> str: """Process image transformation with Gemini. Args: source_image: PIL Image object to transform optimized_edit_prompt: Optimized text prompt for transformation original_edit_prompt: Original user prompt for naming Returns: Path to the transformed image file """ # Create prompt for image transformation edit_instructions = get_image_transformation_prompt(optimized_edit_prompt) # Process with Gemini and return the result return await process_image_with_gemini( [edit_instructions, source_image], original_edit_prompt )