import os
import gradio as gr
import pinecone
from gpt_index import GPTIndexMemory, GPTPineconeIndex

from langchain.agents import Tool
from langchain.chains.conversation.memory import ConversationBufferMemory
from langchain import OpenAI
from langchain.agents import initialize_agent

OPENAI_API_KEY=os.environ.get("OPENAI_API_KEY")
PINECONE_API_KEY=os.environ.get("PINECONE_API_KEY")
PINECONE_ENV=os.environ.get("PINECONE_ENV")

pindex=pinecone.Index("sethgodin")
pinedex=GPTPineconeIndex([], pinecone_index=pindex)

tools = [
    Tool(
        name = "GPT Index",
        func=lambda q: str(pinedex.query(q)),
        description="useful for when you want to answer questions about the author. The input to this tool should be a complete english sentence.",
        return_direct=True
    ),
]
memory = ConversationBufferMemory(memory_key="chat_history")
llm=OpenAI(temperature=0)
agent_chain = initialize_agent(tools, llm, agent="conversational-react-description", memory=memory)

def predict(input, history=[]):
    # generate a response 
    history = agent_chain.run(input=input)
    response = [(response[i], response[i+1]) for i in range(0, len(response)-1, 2)]  # convert to tuples of list
    return response, history

with gr.Blocks() as demo:
    chatbot = gr.Chatbot()
    state = gr.State([])

    with gr.Row():
        txt = gr.Textbox(show_label=False, placeholder="Enter text and press enter").style(container=False)

    txt.submit(predict, [txt, state], [chatbot, state])

demo.launch()