feat(refactor): changed variable names

examples/gemini/json_scraper_gemini.py

@@ -1,10 +1,10 @@
 """
-Basic example of scraping pipeline using SmartScraper from JSON documents
+Basic example of scraping pipeline using JSONScraperGraph from JSON documents
 """
 
 import os
 from dotenv import load_dotenv
-from scrapegraphai.graphs import JsonScraperGraph
+from scrapegraphai.graphs import JSONScraperGraph
 from scrapegraphai.utils import convert_to_csv, convert_to_json, prettify_exec_info
 load_dotenv()
 
@@ -33,23 +33,23 @@
 }
 
 # ************************************************
-# Create the JsonScraperGraph instance and run it
+# Create the JSONScraperGraph instance and run it
 # ************************************************
 
-smart_scraper_graph = JsonScraperGraph(
+json_scraper_graph = JSONScraperGraph(
     prompt="List me all the authors, title and genres of the books",
     source=text,  # Pass the content of the file, not the file object
     config=graph_config
 )
 
-result = smart_scraper_graph.run()
+result = json_scraper_graph.run()
 print(result)
 
 # ************************************************
 # Get graph execution info
 # ************************************************
 
-graph_exec_info = smart_scraper_graph.get_execution_info()
+graph_exec_info = json_scraper_graph.get_execution_info()
 print(prettify_exec_info(graph_exec_info))
 
 # Save to json or csv

examples/gemini/xml_scraper_openai.py

@@ -1,10 +1,10 @@
 """
-Basic example of scraping pipeline using XmlScraperGraph from XML documents
+Basic example of scraping pipeline using XMLScraperGraph from XML documents
 """
 
 import os
 from dotenv import load_dotenv
-from scrapegraphai.graphs import XmlScraperGraph
+from scrapegraphai.graphs import XMLScraperGraph
 from scrapegraphai.utils import convert_to_csv, convert_to_json, prettify_exec_info
 load_dotenv()
 
@@ -33,23 +33,23 @@
 }
 
 # ************************************************
-# Create the XmlScraperGraph instance and run it
+# Create the XMLScraperGraph instance and run it
 # ************************************************
 
-smart_scraper_graph = XmlScraperGraph(
+xml_scraper_graph = XMLScraperGraph(
     prompt="List me all the authors, title and genres of the books",
     source=text,  # Pass the content of the file, not the file object
     config=graph_config
 )
 
-result = smart_scraper_graph.run()
+result = xml_scraper_graph.run()
 print(result)
 
 # ************************************************
 # Get graph execution info
 # ************************************************
 
-graph_exec_info = smart_scraper_graph.get_execution_info()
+graph_exec_info = xml_scraper_graph.get_execution_info()
 print(prettify_exec_info(graph_exec_info))
 
 # Save to json or csv

examples/local_models/Docker/json_scraper_docker.py

@@ -1,10 +1,10 @@
 """
-Basic example of scraping pipeline using JsonScraperGraph from JSON documents
+Basic example of scraping pipeline using JSONScraperGraph from JSON documents
 """
 
 import os
 from dotenv import load_dotenv
-from scrapegraphai.graphs import JsonScraperGraph
+from scrapegraphai.graphs import JSONScraperGraph
 from scrapegraphai.utils import convert_to_csv, convert_to_json, prettify_exec_info
 load_dotenv()
 
@@ -37,23 +37,23 @@
 }
 
 # ************************************************
-# Create the JsonScraperGraph instance and run it
+# Create the JSONScraperGraph instance and run it
 # ************************************************
 
-smart_scraper_graph = JsonScraperGraph(
+json_scraper_graph = JSONScraperGraph(
     prompt="List me all the authors, title and genres of the books",
     source=text,  # Pass the content of the file, not the file object
     config=graph_config
 )
 
-result = smart_scraper_graph.run()
+result = json_scraper_graph.run()
 print(result)
 
 # ************************************************
 # Get graph execution info
 # ************************************************
 
-graph_exec_info = smart_scraper_graph.get_execution_info()
+graph_exec_info = json_scraper_graph.get_execution_info()
 print(prettify_exec_info(graph_exec_info))
 
 # Save to json or csv

examples/local_models/Docker/xml_scraper_docker.py

@@ -1,10 +1,10 @@
 """
-Basic example of scraping pipeline using XmlScraperGraph from XML documents
+Basic example of scraping pipeline using XMLScraperGraph from XML documents
 """
 
 import os
 from dotenv import load_dotenv
-from scrapegraphai.graphs import XmlScraperGraph
+from scrapegraphai.graphs import XMLScraperGraph
 from scrapegraphai.utils import convert_to_csv, convert_to_json, prettify_exec_info
 load_dotenv()
 
@@ -37,23 +37,23 @@
 }
 
 # ************************************************
-# Create the XmlScraperGraph instance and run it
+# Create the XMLScraperGraph instance and run it
 # ************************************************
 
-smart_scraper_graph = XmlScraperGraph(
+xml_scraper_graph = XMLScraperGraph(
     prompt="List me all the authors, title and genres of the books",
     source=text,  # Pass the content of the file, not the file object
     config=graph_config
 )
 
-result = smart_scraper_graph.run()
+result = xml_scraper_graph.run()
 print(result)
 
 # ************************************************
 # Get graph execution info
 # ************************************************
 
-graph_exec_info = smart_scraper_graph.get_execution_info()
+graph_exec_info = xml_scraper_graph.get_execution_info()
 print(prettify_exec_info(graph_exec_info))
 
 # Save to json or csv

examples/local_models/Ollama/json_scraper_ollama.py

@@ -1,10 +1,10 @@
 """
-Basic example of scraping pipeline using JsonScraperGraph from JSON documents
+Basic example of scraping pipeline using JSONScraperGraph from JSON documents
 """
 
 import os
 from dotenv import load_dotenv
-from scrapegraphai.graphs import JsonScraperGraph
+from scrapegraphai.graphs import JSONScraperGraph
 from scrapegraphai.utils import convert_to_csv, convert_to_json, prettify_exec_info
 load_dotenv()
 
@@ -39,23 +39,23 @@
 }
 
 # ************************************************
-# Create the XmlScraperGraph instance and run it
+# Create the JSONScraperGraph instance and run it
 # ************************************************
 
-smart_scraper_graph = JsonScraperGraph(
+json_scraper_graph = JSONScraperGraph(
     prompt="List me all the authors, title and genres of the books",
     source=text,  # Pass the content of the file, not the file object
     config=graph_config
 )
 
-result = smart_scraper_graph.run()
+result = json_scraper_graph.run()
 print(result)
 
 # ************************************************
 # Get graph execution info
 # ************************************************
 
-graph_exec_info = smart_scraper_graph.get_execution_info()
+graph_exec_info = json_scraper_graph.get_execution_info()
 print(prettify_exec_info(graph_exec_info))
 
 # Save to json or csv

examples/local_models/Ollama/xml_scraper_ollama.py

@@ -1,10 +1,10 @@
 """
-Basic example of scraping pipeline using XmlScraperGraph from XML documents
+Basic example of scraping pipeline using XMLScraperGraph from XML documents
 """
 
 import os
 from dotenv import load_dotenv
-from scrapegraphai.graphs import XmlScraperGraph
+from scrapegraphai.graphs import XMLScraperGraph
 from scrapegraphai.utils import convert_to_csv, convert_to_json, prettify_exec_info
 load_dotenv()
 
@@ -39,23 +39,23 @@
 }
 
 # ************************************************
-# Create the XmlScraperGraph instance and run it
+# Create the XMLScraperGraph instance and run it
 # ************************************************
 
-smart_scraper_graph = XmlScraperGraph(
+xml_scraper_graph = XMLScraperGraph(
     prompt="List me all the authors, title and genres of the books",
     source=text,  # Pass the content of the file, not the file object
     config=graph_config
 )
 
-result = smart_scraper_graph.run()
+result = xml_scraper_graph.run()
 print(result)
 
 # ************************************************
 # Get graph execution info
 # ************************************************
 
-graph_exec_info = smart_scraper_graph.get_execution_info()
+graph_exec_info = xml_scraper_graph.get_execution_info()
 print(prettify_exec_info(graph_exec_info))
 
 # Save to json or csv

examples/local_models/result.json

@@ -0,0 +1 @@
+{"projects": [{"title": "Rotary Pendulum RL", "description": "Open Source project aimed at controlling a real life rotary pendulum using RL algorithms"}, {"title": "DQN Implementation from scratch", "description": "Developed a Deep Q-Network algorithm to train a simple and double pendulum"}, {"title": "Multi Agents HAED", "description": "University project which focuses on simulating a multi-agent system to perform environment mapping. Agents, equipped with sensors, explore and record their surroundings, considering uncertainties in their readings."}, {"title": "Wireless ESC for Modular Drones", "description": "Modular drone architecture proposal and proof of concept. The project received maximum grade."}]}

examples/openai/json_scraper_openai.py

@@ -1,10 +1,10 @@
 """
-Basic example of scraping pipeline using JsonScraperGraph from JSON documents
+Basic example of scraping pipeline using JSONScraperGraph from JSON documents
 """
 
 import os
 from dotenv import load_dotenv
-from scrapegraphai.graphs import JsonScraperGraph
+from scrapegraphai.graphs import JSONScraperGraph
 from scrapegraphai.utils import convert_to_csv, convert_to_json, prettify_exec_info
 load_dotenv()
 
@@ -33,23 +33,23 @@
 }
 
 # ************************************************
-# Create the XmlScraperGraph instance and run it
+# Create the JSONScraperGraph instance and run it
 # ************************************************
 
-smart_scraper_graph = JsonScraperGraph(
+json_scraper_graph = JSONScraperGraph(
     prompt="List me all the authors, title and genres of the books",
     source=text,  # Pass the content of the file, not the file object
     config=graph_config
 )
 
-result = smart_scraper_graph.run()
+result = json_scraper_graph.run()
 print(result)
 
 # ************************************************
 # Get graph execution info
 # ************************************************
 
-graph_exec_info = smart_scraper_graph.get_execution_info()
+graph_exec_info = json_scraper_graph.get_execution_info()
 print(prettify_exec_info(graph_exec_info))
 
 # Save to json or csv

examples/openai/result.json

@@ -0,0 +1 @@
+{"top_5_eyeliner_products_for_gift": [{"product_name": "Tarte Double Take Eyeliner", "type": "Liquid, Gel", "price": "$26", "link": "https://www.sephora.com/product/double-take-eyeliner-P421701"}, {"product_name": "AppleDoll Velvet Liner", "type": "Liquid", "price": "$22", "link": "https://www.appledoll.com/products/velvet-liner"}, {"product_name": "Rare Beauty Perfect Strokes Gel Eyeliner", "type": "Gel", "price": "$19", "link": "https://www.sephora.com/product/perfect-strokes-gel-eyeliner-P468000"}, {"product_name": "Laura Mercier Caviar Tightline Eyeliner", "type": "Gel", "price": "$29", "link": "https://www.sephora.com/product/caviar-tightline-eyeliner-P448800"}, {"product_name": "Ilia Clean Line Liquid Eyeliner", "type": "Liquid", "price": "$28", "link": "https://www.amazon.com/ILIA-Clean-Line-Liquid-Eyeliner/dp/B08Z7JZQZP"}, {"brand": "Tom Ford", "product_name": "Eye Defining Pen", "price": "$62", "type": "Liquid", "colors": 1, "retailer": "Nordstrom"}, {"brand": "Fenty Beauty", "product_name": "Flyliner", "price": "$24", "type": "Liquid", "colors": 2, "retailer": "Sephora"}, {"brand": "Lanc\u00f4me", "product_name": "Le Crayon Kh\u00f4l Smoky Eyeliner", "price": "$28", "type": "Kohl", "colors": 2, "retailer": "Macy's"}, {"brand": "Jillian Dempsey", "product_name": "Kh\u00f4l Eyeliner", "price": "$20", "type": "Kohl", "colors": 6, "retailer": "Amazon"}, {"brand": "R\u00f3en", "product_name": "Eyeline Define Eyeliner Pencil", "price": "$26", "type": "Kohl", "colors": 4, "retailer": "Credo Beauty"}]}

examples/openai/xml_scraper_openai.py

@@ -1,10 +1,10 @@
 """
-Basic example of scraping pipeline using XmlScraperGraph from XML documents
+Basic example of scraping pipeline using XMLScraperGraph from XML documents
 """
 
 import os
 from dotenv import load_dotenv
-from scrapegraphai.graphs import XmlScraperGraph
+from scrapegraphai.graphs import XMLScraperGraph
 from scrapegraphai.utils import convert_to_csv, convert_to_json, prettify_exec_info
 load_dotenv()
 
@@ -33,23 +33,23 @@
 }
 
 # ************************************************
-# Create the XmlScraperGraph instance and run it
+# Create the XMLScraperGraph instance and run it
 # ************************************************
 
-smart_scraper_graph = XmlScraperGraph(
+xml_scraper_graph = XMLScraperGraph(
     prompt="List me all the authors, title and genres of the books",
     source=text,  # Pass the content of the file, not the file object
     config=graph_config
 )
 
-result = smart_scraper_graph.run()
+result = xml_scraper_graph.run()
 print(result)
 
 # ************************************************
 # Get graph execution info
 # ************************************************
 
-graph_exec_info = smart_scraper_graph.get_execution_info()
+graph_exec_info = xml_scraper_graph.get_execution_info()
 print(prettify_exec_info(graph_exec_info))
 
 # Save to json or csv

scrapegraphai/graphs/__init__.py

@@ -6,5 +6,5 @@
 from .speech_graph import SpeechGraph
 from .search_graph import SearchGraph
 from .script_creator_graph import ScriptCreatorGraph
-from .xml_scraper_graph import XmlScraperGraph
-from .json_scraper_graph import JsonScraperGraph
+from .xml_scraper_graph import XMLScraperGraph
+from .json_scraper_graph import JSONScraperGraph

scrapegraphai/graphs/json_scraper_graph.py

@@ -11,7 +11,7 @@
 from .abstract_graph import AbstractGraph
 
 
-class JsonScraperGraph(AbstractGraph):
+class JSONScraperGraph(AbstractGraph):
     """
     SmartScraper is a comprehensive web scraping tool that automates the process of extracting
     information from web pages using a natural language model to interpret and answer prompts.

scrapegraphai/graphs/xml_scraper_graph.py

@@ -11,7 +11,7 @@
 from .abstract_graph import AbstractGraph
 
 
-class XmlScraperGraph(AbstractGraph):
+class XMLScraperGraph(AbstractGraph):
     """
     SmartScraper is a comprehensive web scraping tool that automates the process of extracting
     information from web pages using a natural language model to interpret and answer prompts.