import os
import openai

os.environ["TOKENIZERS_PARALLELISM"] = "false"
os.environ["OPENAI_API_KEY"]
os.environ["GROQ_API_KEY"]

global agent


def create_agent():

    from langchain_groq import ChatGroq
    from langchain.chains.conversation.memory import ConversationSummaryBufferMemory
    from langchain.chains import ConversationChain

    global agent

    llm = ChatGroq(temperature=0, model_name="mixtral-8x7b-32768")
    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,k):

    from langchain_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(
        "/home/user/app/docs_db/", 
        embeddings, 
        allow_dangerous_deserialization=True
    )
    docs = docs_db.similarity_search(question,int(k))

    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>Your AI Medical Assistant</center>")

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

            with gr.Tab("Query Documents"):
                with gr.Column():
                    create_agent_button = gr.Button("Create Agent")
                    create_agent_output = gr.Textbox(label="Output")

                    docs_prompt_input = gr.Textbox(label="Custom Prompt")
                    k=gr.Textbox(label="Number of Chunks")

                    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, create_agent_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,k],
        outputs=[docs_chatbot, docs_state],
    )

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

demo.queue()
demo.launch()