Auto-debugging Python code with GPT4

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! :)

#%%

"""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
    

    except FileNotFoundError:
        print(f"Error: Could not find file '{input_file}'")
        sys.exit(1)

"""code execution"""

if __name__ == "__main__": ingest_current_page()