Screenshot scraper integration

examples/openai/screenshot_scraper.py

@@ -0,0 +1,38 @@
+""" 
+Basic example of scraping pipeline using SmartScraper
+"""
+
+import os
+import json
+from dotenv import load_dotenv
+from scrapegraphai.graphs import ScreenshotScraperGraph
+from scrapegraphai.utils import prettify_exec_info
+
+load_dotenv()
+
+# ************************************************
+# Define the configuration for the graph
+# ************************************************
+
+
+graph_config = {
+    "llm": {
+        "api_key": os.getenv("OPENAI_API_KEY"),
+        "model": "gpt-4o",
+    },
+    "verbose": True,
+    "headless": False,
+}
+
+# ************************************************
+# Create the ScreenshotScraperGraph instance and run it
+# ************************************************
+
+smart_scraper_graph = ScreenshotScraperGraph(
+    prompt="List me all the projects",
+    source="https://perinim.github.io/projects/",
+    config=graph_config
+)
+
+result = smart_scraper_graph.run()
+print(json.dumps(result, indent=4))

examples/openai/smart_scraper_openai.py

@@ -2,10 +2,12 @@
 Basic example of scraping pipeline using SmartScraper
 """
 
-import os, json
+import os
+import json
+from dotenv import load_dotenv
 from scrapegraphai.graphs import SmartScraperGraph
 from scrapegraphai.utils import prettify_exec_info
-from dotenv import load_dotenv
+
 load_dotenv()
 
 # ************************************************
@@ -16,7 +18,7 @@
 graph_config = {
     "llm": {
         "api_key": os.getenv("OPENAI_API_KEY"),
-        "model": "gpt-3.5-turbo",
+        "model": "gpt-4o",
     },
     "verbose": True,
     "headless": False,

scrapegraphai/graphs/__init__.py

@@ -24,3 +24,4 @@
 from .markdown_scraper_graph import MDScraperGraph
 from .markdown_scraper_multi_graph import MDScraperMultiGraph
 from .search_link_graph import SearchLinkGraph
+from .screenshot_scraper_graph import ScreenshotScraperGraph

scrapegraphai/graphs/screenshot_scraper_graph.py

@@ -0,0 +1,82 @@
+""" 
+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=["imgs"],
+            node_config={
+                "link": self.source
+            }
+        )
+        generate_answer_from_image_node = GenerateAnswerFromImageNode(
+            input="imgs",
+            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.")
+

scrapegraphai/nodes/__init__.py

@@ -19,4 +19,6 @@
 from .graph_iterator_node import GraphIteratorNode
 from .merge_answers_node import MergeAnswersNode
 from .generate_answer_omni_node import GenerateAnswerOmniNode
-from .merge_generated_scripts import MergeGeneratedScriptsNode 
+from .merge_generated_scripts import MergeGeneratedScriptsNode
+from .fetch_screen_node import FetchScreenNode
+from .generate_answer_from_image_node import GenerateAnswerFromImageNode

scrapegraphai/nodes/fetch_screen_node.py

@@ -0,0 +1,55 @@
+"""
+fetch_screen_node module
+"""
+from typing import List, Optional
+from playwright.sync_api import sync_playwright
+from .base_node import BaseNode
+from ..utils.logging import get_logger
+
+class FetchScreenNode(BaseNode):
+    """
+    FetchScreenNode captures screenshots from a given URL and stores the image data as bytes.
+    """
+
+    def __init__(
+        self,
+        input: str,
+        output: List[str],
+        node_config: Optional[dict] = None,
+        node_name: str = "FetchScreenNode",
+    ):
+        super().__init__(node_name, "node", input, output, 2, node_config)
+        self.url = node_config.get("link")
+
+    def execute(self, state: dict) -> dict:
+        """
+        Captures screenshots from the input URL and stores them in the state dictionary as bytes.
+        """
+        self.logger.info(f"--- Executing {self.node_name} Node ---")
+
+        with sync_playwright() as p:
+            browser = p.chromium.launch()
+            page = browser.new_page()
+            page.goto(self.url)
+
+            viewport_height = page.viewport_size["height"]
+
+            screenshot_counter = 1
+
+            screenshot_data_list = []
+
+            def capture_screenshot(scroll_position, counter):
+                page.evaluate(f"window.scrollTo(0, {scroll_position});")
+                screenshot_data = page.screenshot()
+                screenshot_data_list.append(screenshot_data)
+
+            capture_screenshot(0, screenshot_counter)
+            screenshot_counter += 1
+            capture_screenshot(viewport_height, screenshot_counter)
+
+            browser.close()
+
+        state["link"] = self.url
+        state['screenshots'] = screenshot_data_list
+
+        return state

scrapegraphai/nodes/generate_answer_from_image_node.py

@@ -0,0 +1,99 @@
+import base64
+import asyncio
+from typing import List, Optional
+import aiohttp
+from .base_node import BaseNode
+from ..utils.logging import get_logger
+
+class GenerateAnswerFromImageNode(BaseNode):
+    """
+    GenerateAnswerFromImageNode analyzes images from the state dictionary using the OpenAI API
+    and updates the state with the consolidated answers.
+    """
+
+    def __init__(
+        self,
+        input: str,
+        output: List[str],
+        node_config: Optional[dict] = None,
+        node_name: str = "GenerateAnswerFromImageNode",
+    ):
+        super().__init__(node_name, "node", input, output, 2, node_config)
+
+    async def process_image(self, session, api_key, image_data, user_prompt):
+        # Convert image data to base64
+        base64_image = base64.b64encode(image_data).decode('utf-8')
+
+        headers = {
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {api_key}"
+        }
+
+        payload = {
+            "model": self.node_config["config"]["llm"]["model"],
+            "messages": [
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "text",
+                            "text": user_prompt
+                        },
+                        {
+                            "type": "image_url",
+                            "image_url": {
+                                "url": f"data:image/jpeg;base64,{base64_image}"
+                            }
+                        }
+                    ]
+                }
+            ],
+            "max_tokens": 300
+        }
+
+        async with session.post("https://api.openai.com/v1/chat/completions",
+                                headers=headers, json=payload) as response:
+            result = await response.json()
+            return result.get('choices', [{}])[0].get('message', {}).get('content', 'No response')
+
+    async def execute_async(self, state: dict) -> dict:
+        """
+        Processes images from the state, generates answers, 
+        consolidates the results, and updates the state asynchronously.
+        """
+        self.logger.info(f"--- Executing {self.node_name} Node ---")
+
+        images = state.get('screenshots', [])
+        analyses = []
+
+        supported_models = ("gpt-4o", "gpt-4o-mini", "gpt-4-turbo")
+
+        if self.node_config["config"]["llm"]["model"] not in supported_models:
+            raise ValueError(f"""Model '{self.node_config['config']['llm']['model']}'
+                             is not supported. Supported models are: 
+                             {', '.join(supported_models)}.""")
+
+        api_key = self.node_config.get("config", {}).get("llm", {}).get("api_key", "")
+
+        async with aiohttp.ClientSession() as session:
+            tasks = [
+                self.process_image(session, api_key, image_data,
+                                   state.get("user_prompt", "Extract information from the image"))
+                for image_data in images
+            ]
+
+            analyses = await asyncio.gather(*tasks)
+
+        consolidated_analysis = " ".join(analyses)
+
+        state['answer'] = {
+            "consolidated_analysis": consolidated_analysis
+        }
+
+        return state
+
+    def execute(self, state: dict) -> dict:
+        """
+        Wrapper to run the asynchronous execute_async function in a synchronous context.
+        """
+        return asyncio.run(self.execute_async(state))

tests/graphs/screenshot_scraper_test.py

@@ -0,0 +1,39 @@
+import os
+import pytest
+import json
+from scrapegraphai.graphs import ScreenshotScraperGraph
+from dotenv import load_dotenv
+
+# Load environment variables
+load_dotenv()
+
+# Define a fixture for the graph configuration
+@pytest.fixture
+def graph_config():
+    """
+    Creation of the graph
+    """
+    return {
+        "llm": {
+            "api_key": os.getenv("OPENAI_API_KEY"),
+            "model": "gpt-4o",
+        },
+        "verbose": True,
+        "headless": False,
+    }
+
+def test_screenshot_scraper_graph(graph_config):
+    """
+    test
+    """
+    smart_scraper_graph = ScreenshotScraperGraph(
+        prompt="List me all the projects",
+        source="https://perinim.github.io/projects/",
+        config=graph_config
+    )
+
+    result = smart_scraper_graph.run()
+
+    assert result is not None, "The result should not be None"
+
+    print(json.dumps(result, indent=4))