Scrapegraph-ai/scrapegraphai/graphs/screenshot_scraper_graph.py
2024-10-08 08:54:18 +02:00

83 lines
2.6 KiB
Python

"""
ScreenshotScraperGraph Module
"""
from typing import Optional
import logging
from pydantic import BaseModel
from .base_graph import BaseGraph
from .abstract_graph import AbstractGraph
from ..nodes import (FetchScreenNode, GenerateAnswerFromImageNode)
class ScreenshotScraperGraph(AbstractGraph):
"""
A graph instance representing the web scraping workflow for images.
Attributes:
prompt (str): The input text to be scraped.
config (dict): Configuration parameters for the graph.
source (str): The source URL or image link to scrape from.
Methods:
__init__(prompt: str, source: str, config: dict, schema: Optional[BaseModel] = None)
Initializes the ScreenshotScraperGraph instance with the given prompt,
source, and configuration parameters.
_create_graph()
Creates a graph of nodes representing the web scraping workflow for images.
run()
Executes the scraping process and returns the answer to the prompt.
"""
def __init__(self, prompt: str, source: str, config: dict, schema: Optional[BaseModel] = None):
super().__init__(prompt, config, source, schema)
def _create_graph(self) -> BaseGraph:
"""
Creates the graph of nodes representing the workflow for web scraping with images.
Returns:
BaseGraph: A graph instance representing the web scraping workflow for images.
"""
fetch_screen_node = FetchScreenNode(
input="url",
output=["screenshots"],
node_config={
"link": self.source
}
)
generate_answer_from_image_node = GenerateAnswerFromImageNode(
input="screenshots",
output=["answer"],
node_config={
"config": self.config
}
)
return BaseGraph(
nodes=[
fetch_screen_node,
generate_answer_from_image_node,
],
edges=[
(fetch_screen_node, generate_answer_from_image_node),
],
entry_point=fetch_screen_node,
graph_name=self.__class__.__name__
)
def run(self) -> str:
"""
Executes the scraping process and returns the answer to the prompt.
Returns:
str: The answer to the prompt.
"""
inputs = {"user_prompt": self.prompt}
self.final_state, self.execution_info = self.graph.execute(inputs)
return self.final_state.get("answer", "No answer found.")