add screenshot scraper

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 the email of the company",
+    source="https://scrapegraphai.com/",
+    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 @@ load_dotenv()
 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 .script_creator_multi_graph import ScriptCreatorMultiGraph
 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 +1,71 @@
+"""
+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):
+    """
+   smart_scraper.run()
+        )
+    """
+
+    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.
+
+        Returns:
+            BaseGraph: A graph instance representing the web scraping workflow.
+        """
+        fetch_screen_node = FetchScreenNode(
+            input="url",
+            output=["imgs"],
+            node_config={
+                "link": self.source
+            }
+        )
+        generate_answer_from_image_node = GenerateAnswerFromImageNode(
+            input="doc",
+            output=["parsed_doc"],
+            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 .generate_answer_pdf_node import GenerateAnswerPDFNode
 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 +1,56 @@
+from typing import List, Optional
+from playwright.sync_api import sync_playwright
+from .base_node import BaseNode
+
+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."""
+        
+        screenshots = []
+
+        with sync_playwright() as p:
+            browser = p.chromium.launch()
+            page = browser.new_page()
+            page.goto(self.url)
+
+            viewport_height = page.viewport_size["height"]
+
+            # Initialize screenshot counter
+            screenshot_counter = 1
+
+            # List to keep track of screenshot data
+            screenshot_data_list = []
+
+            # Function to capture screenshots
+            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 screenshots
+            capture_screenshot(0, screenshot_counter)  # First screenshot
+            screenshot_counter += 1
+            capture_screenshot(viewport_height, screenshot_counter)  # Second screenshot
+
+            browser.close()
+
+        # Store screenshot data as bytes in the state dictionary
+        for screenshot_data in screenshot_data_list:
+            screenshots.append(screenshot_data)
+        state["link"] = self.url
+        state['screenshots'] = screenshots
+        return state

scrapegraphai/nodes/generate_answer_from_image_node.py

@@ -0,0 +1,74 @@
+from typing import List, Optional
+from .base_node import BaseNode
+import base64
+import requests
+
+class GenerateAnswerFromImageNode(BaseNode):
+    """
+    GenerateAnswerFromImageNode analyzes images from the state dictionary using the OpenAI API
+    and updates the state with the generated 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)
+
+    def execute(self, state: dict) -> dict:
+        """Processes images from the state, generates answers, and updates the state."""
+        # Retrieve the image data from the state dictionary
+        images = state.get('screenshots', [])
+        results = []
+
+        # OpenAI API Key
+        for image_data in images:
+            # Encode the image data to base64
+            base64_image = base64.b64encode(image_data).decode('utf-8')
+
+            # Prepare API request
+            headers = {
+                "Content-Type": "application/json",
+                "Authorization": f"Bearer {self.node_config.get("config").get("llm").get("api_key")}"
+            }
+
+            payload = {
+                "model": "gpt-4o-mini",
+                "messages": [
+                    {
+                        "role": "user",
+                        "content": [
+                            {
+                                "type": "text",
+                                "text": state.get("user_prompt", "Extract information from the image")
+                            },
+                            {
+                                "type": "image_url",
+                                "image_url": {
+                                    "url": f"data:image/jpeg;base64,{base64_image}"
+                                }
+                            }
+                        ]
+                    }
+                ],
+                "max_tokens": 300
+            }
+
+            # Make the API request
+            response = requests.post("https://api.openai.com/v1/chat/completions", headers=headers, json=payload)
+            result = response.json()
+
+            # Extract the response text
+            response_text = result.get('choices', [{}])[0].get('message', {}).get('content', 'No response')
+
+            # Append the result to the results list
+            results.append({
+                "analysis": response_text
+            })
+
+        # Update the state dictionary with the results
+        state['answer'] = results
+        return state