import os
import openai

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

    import shutil
    import os

    output_dir="/content/docs/"

    if os.path.exists(output_dir):
        shutil.rmtree(output_dir)

    if not os.path.exists(output_dir):
        os.makedirs(output_dir)

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

    return "Successful!"

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.document_loaders.csv_loader import CSVLoader
    from langchain.document_loaders import UnstructuredExcelLoader
    from langchain.vectorstores import FAISS
    from langchain.embeddings.openai import OpenAIEmbeddings
    from langchain.text_splitter import RecursiveCharacterTextSplitter

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

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

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

    loader4 = DirectoryLoader('/content/docs/', glob="./*.csv", loader_cls=CSVLoader)
    document4 = loader4.load()

    loader5 = DirectoryLoader('/content/docs/', glob="./*.xlsx", loader_cls=UnstructuredExcelLoader)
    document5 = loader5.load()

    document1.extend(document2)
    document1.extend(document3)
    document1.extend(document4)
    document1.extend(document5)

    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("/content/docs_db/")

    return "Successful!"

global agent

def create_agent():

    from langchain.chat_models import ChatOpenAI
    from langchain.chains.conversation.memory import ConversationSummaryBufferMemory
    from langchain.chains import ConversationChain
    global agent

    llm = ChatOpenAI(model_name='gpt-3.5-turbo-16k')
    memory = ConversationSummaryBufferMemory(llm=llm, max_token_limit=1000)
    agent = ConversationChain(llm=llm, memory=memory, verbose=True)

    return "Successful!"

def formatted_response(docs, question, response, state):

    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}"

    state.append((question, formatted_output))
    return state, state

def search_docs(prompt, question, state):

    from langchain.embeddings.openai import OpenAIEmbeddings
    from langchain.vectorstores import FAISS
    from langchain.callbacks import get_openai_callback
    global agent
    agent = agent

    state = state or []

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

    prompt += "\n\n"
    prompt += question
    prompt += "\n\n"
    prompt += str(docs)

    with get_openai_callback() as cb:
        response = agent.predict(input=prompt)
        print(cb)

    return formatted_response(docs, question, response, state)

import gradio as gr

css = """
.col{
    max-width: 75%;
    margin: 0 auto;
    display: flex;
    flex-direction: column;
    justify-content: center;
    align-items: center;
}
"""

with gr.Blocks(css=css) as demo:
    gr.Markdown("## <center>All in One Document Chatting App</center>")

    with gr.Tab("Chat With Your Documents"):
      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")

            create_agent_button = gr.Button("Create Agent")
            create_agent_output = gr.Textbox(label="Output")

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

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

            docs_prompt_input = gr.Textbox(label="Custom Prompt")

            docs_chatbot = gr.Chatbot(label="Chats")
            docs_state = gr.State()

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

            gr.ClearButton([docs_prompt_input, docs_search_input])

    #########################################################################################################

    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)
    create_agent_button.click(create_agent, inputs=None, outputs=create_agent_output)

    docs_search_button.click(search_docs, inputs=[docs_prompt_input, docs_search_input, docs_state], outputs=[docs_chatbot, docs_state])

    #########################################################################################################

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