Scrapegraph-ai/scrapegraphai/nodes/image_to_text_node.py
2024-05-08 14:56:44 +02:00

55 lines
1.9 KiB
Python

"""
ImageToTextNode Module
"""
from typing import List, Optional
from .base_node import BaseNode
class ImageToTextNode(BaseNode):
"""
Retrieve an image from an URL and convert it to text using an ImageToText model.
Attributes:
llm_model: An instance of the language model client used for image-to-text conversion.
verbose (bool): A flag indicating whether to show print statements during execution.
Args:
input (str): Boolean expression defining the input keys needed from the state.
output (List[str]): List of output keys to be updated in the state.
node_config (dict): Additional configuration for the node.
node_name (str): The unique identifier name for the node, defaulting to "ImageToText".
"""
def __init__(self, input: str, output: List[str], node_config: Optional[dict]=None,
node_name: str = "ImageToText"):
super().__init__(node_name, "node", input, output, 1, node_config)
self.llm_model = node_config["llm_model"]
self.verbose = False if node_config is None else node_config.get("verbose", False)
def execute(self, state: dict) -> dict:
"""
Generate text from an image using an image-to-text model. The method retrieves the image
from the URL provided in the state.
Args:
state (dict): The current state of the graph. The input keys will be used to fetch the
correct data types from the state.
Returns:
dict: The updated state with the input key containing the text extracted from the image.
"""
if self.verbose:
print("---GENERATING TEXT FROM IMAGE---")
input_keys = self.get_input_keys(state)
input_data = [state[key] for key in input_keys]
url = input_data[0]
text_answer = self.llm_model.run(url)
state.update({"image_text": text_answer})
return state