import os
import openai

os.environ["TOKENIZERS_PARALLELISM"] = "false"
os.environ["OPENAI_API_KEY"]
def save_docs(docs):

    import shutil
    import os

    destination_dir = "/kaggle/working/docs/"
    os.makedirs(destination_dir, exist_ok=True)

    output_dir="/kaggle/working/docs/"

    for doc in docs:
      shutil.copy(doc.name, output_dir)

    return "File(s) saved successfully!"

def process_docs():

    from langchain.document_loaders import PyPDFLoader
    from langchain.document_loaders import DirectoryLoader
    from langchain.document_loaders import TextLoader
    from langchain.document_loaders import Docx2txtLoader
    from langchain.vectorstores import FAISS
    from langchain.embeddings.openai import OpenAIEmbeddings
    from langchain.text_splitter import RecursiveCharacterTextSplitter

    loader1 = DirectoryLoader('/kaggle/working/docs/', glob="./*.pdf", loader_cls=PyPDFLoader)
    document1 = loader1.load()

    loader2 = DirectoryLoader('/kaggle/working/docs/', glob="./*.txt", loader_cls=TextLoader)
    document2 = loader2.load()

    loader3 = DirectoryLoader('/kaggle/working/docs/', glob="./*.docx", loader_cls=Docx2txtLoader)
    document3 = loader3.load()

    document1.extend(document2)
    document1.extend(document3)

    text_splitter = RecursiveCharacterTextSplitter(
        chunk_size=1000,
        chunk_overlap=200,
        length_function=len
    )

    docs = text_splitter.split_documents(document1)
    embeddings = OpenAIEmbeddings()

    docs_db = FAISS.from_documents(docs, embeddings)
    docs_db.save_local("/kaggle/working/docs_db/")

    return "File(s) processed successfully!"

def formatted_response(docs, response):

    formatted_output = response + "\n\nSources"

    for i, doc in enumerate(docs):
        source_info = doc.metadata.get('source', 'Unknown source')
        page_info = doc.metadata.get('page', None)

        doc_name = source_info.split('/')[-1].strip()

        if page_info is not None:
            formatted_output += f"\n{doc_name}\tpage no {page_info}"
        else:
            formatted_output += f"\n{doc_name}"

    return formatted_output

def search_docs(question):

    from langchain.embeddings.openai import OpenAIEmbeddings
    from langchain.vectorstores import FAISS
    from langchain.chains.question_answering import load_qa_chain
    from langchain.callbacks import get_openai_callback
    from langchain.chat_models import ChatOpenAI

    embeddings = OpenAIEmbeddings()
    docs_db = FAISS.load_local("/kaggle/working/docs_db/", embeddings)
    docs = docs_db.similarity_search(question)

    llm = ChatOpenAI(model_name='gpt-3.5-turbo')
    chain = load_qa_chain(llm, chain_type="stuff")

    with get_openai_callback() as cb:
        response = chain.run(input_documents=docs, question=question)
        print(cb)

    return formatted_response(docs, response)

def delete_docs():

    import shutil

    path1 = "/kaggle/working/docs/"
    path2 = "/kaggle/working/docs_db/"

    try:
        shutil.rmtree(path1)
        shutil.rmtree(path2)
        return "Deleted Successfully"

    except:
        return "Already Deleted"

import gradio as gr

css = """
.col{
    max-width: 50%;
    margin: 0 auto;
    display: flex;
    flex-direction: column;
    justify-content: center;
    align-items: center;
}
"""

with gr.Blocks(css=css) as demo:
    gr.Markdown("## <center>Lawyer GPT</center>")

    with gr.Tab("Your AI Legal Assistant"):
      with gr.Column(elem_classes="col"):

        with gr.Tab("Upload and Process Documents"):
          with gr.Column():

            docs_upload_input = gr.Files(label="Upload File(s)")
            docs_upload_button = gr.Button("Upload")
            docs_upload_output = gr.Textbox(label="Output")

            docs_process_button = gr.Button("Process")
            docs_process_output = gr.Textbox(label="Output")

            gr.ClearButton([docs_upload_input, docs_upload_output, docs_process_output])

        with gr.Tab("Query Documents"):
          with gr.Column():

            docs_search_input = gr.Textbox(label="Enter Question")
            docs_search_button = gr.Button("Search")
            docs_search_output = gr.Textbox(label="Output")

            docs_delete_button = gr.Button("Delete")
            docs_delete_output = gr.Textbox(label="Output")

            gr.ClearButton([docs_search_input, docs_search_output, docs_delete_output])

    #########################################################################################################
    docs_upload_button.click(save_docs, inputs=docs_upload_input, outputs=docs_upload_output)
    docs_process_button.click(process_docs, inputs=None, outputs=docs_process_output)

    docs_search_button.click(search_docs, inputs=docs_search_input, outputs=docs_search_output)

    docs_delete_button.click(delete_docs, inputs=None, outputs=docs_delete_output)
    #########################################################################################################

demo.queue()
demo.launch(debug=True, share=True)