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Auto-debugging Python code with GPT4

3 points by andybar007 3y ago

andybar007 3y ago
AutoDebug Python is an open-source tool that leverages the power of GPT-4 to automatically debug and fix Python scripts. Just put in your API Key and the url of your .py and you’re ready to go. Would love to get your feedback as I can’t code and built this with the help of GPT4. Thanks everyone! :)
check35 3y ago
#%% """imports""" """Load html from files, clean up, split, ingest into Weaviate.""" import pickle import sys from langchain.embeddings import OpenAIEmbeddings from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.vectorstores.faiss import FAISS from langchain.document_loaders.html import UnstructuredHTMLLoader """end of imports""" # %% def ingest_current_page(input_file): """Get documents from web pages.""" try: # Load the path to the current_page.html from pathlib import Path doc_path = Path(input_file).absolute() loader = UnstructuredHTMLLoader(doc_path) raw_page = loader.load() print (f'You have {len(raw_page)} document from the current job application page HTML') print (f'There are {len(raw_page[0].page_content)} characters in your document HTML') """"text splitting""" text_splitter = RecursiveCharacterTextSplitter( chunk_size=100, chunk_overlap=0, ) try: if not all(isinstance(doc.page_content, str) for doc in raw_page): raise TypeError("Error: Input data must be a list of strings") documents = text_splitter.split_documents(raw_page) texts = text_splitter.split_documents(raw_page) except TypeError as e: print(e) sys.exit(1) print ('Splitting current page HTML into chunks') print (f'Now you have {len(texts)} HTML chunk documents for current page.') embeddings = OpenAIEmbeddings() try: vectorstore = FAISS.from_documents(documents, embeddings) except Exception as e: print(f"Error: Failed to vectorize documents. {e}") sys.exit(1) print ('Saving current job application page HTML chunk documents to the vectorstore.pkl file') """saving vectorstore file""" # Save vectorstore with open("vectorstore.pkl", "wb") as f: pickle.dump(vectorstore, f) #print that the HTML chunk documents have been saved to the vectorstore print("HTML chunk documents have been saved to 'vectorstore.pkl'") return vectorstore """error handling""" except FileNotFoundError: print(f"Error: Could not find file '{input_file}'") sys.exit(1) # %% """code execution""" if __name__ == "__main__": ingest_current_page()

3 points by andybar007 3y ago

AutoDebug Python is an open-source tool that leverages the power of GPT-4 to automatically debug and fix Python scripts. Just put in your API Key and the url of your .py and you’re ready to go. Would love to get your feedback as I can’t code and built this with the help of GPT4. Thanks everyone! :)

check35 3y ago

#%% """imports""" """Load html from files, clean up, split, ingest into Weaviate.""" import pickle import sys from langchain.embeddings import OpenAIEmbeddings from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.vectorstores.faiss import FAISS from langchain.document_loaders.html import UnstructuredHTMLLoader """end of imports""" # %% def ingest_current_page(input_file): """Get documents from web pages.""" try: # Load the path to the current_page.html from pathlib import Path doc_path = Path(input_file).absolute() loader = UnstructuredHTMLLoader(doc_path) raw_page = loader.load() print (f'You have {len(raw_page)} document from the current job application page HTML') print (f'There are {len(raw_page[0].page_content)} characters in your document HTML') """"text splitting""" text_splitter = RecursiveCharacterTextSplitter( chunk_size=100, chunk_overlap=0, ) try: if not all(isinstance(doc.page_content, str) for doc in raw_page): raise TypeError("Error: Input data must be a list of strings") documents = text_splitter.split_documents(raw_page) texts = text_splitter.split_documents(raw_page) except TypeError as e: print(e) sys.exit(1) print ('Splitting current page HTML into chunks') print (f'Now you have {len(texts)} HTML chunk documents for current page.') embeddings = OpenAIEmbeddings() try: vectorstore = FAISS.from_documents(documents, embeddings) except Exception as e: print(f"Error: Failed to vectorize documents. {e}") sys.exit(1) print ('Saving current job application page HTML chunk documents to the vectorstore.pkl file') """saving vectorstore file""" # Save vectorstore with open("vectorstore.pkl", "wb") as f: pickle.dump(vectorstore, f) #print that the HTML chunk documents have been saved to the vectorstore print("HTML chunk documents have been saved to 'vectorstore.pkl'") return vectorstore """error handling""" except FileNotFoundError: print(f"Error: Could not find file '{input_file}'") sys.exit(1) # %% """code execution""" if __name__ == "__main__": ingest_current_page()