import gradio as gr
import os
import openai
import gradio as gr
# Get the value of the openai_api_key from environment variable
openai.api_key = os.getenv("OPENAI_API_KEY")

# Import things that are needed generically from langchain
from langchain import LLMMathChain, SerpAPIWrapper
from langchain.agents import AgentType, initialize_agent, load_tools
from langchain.chat_models import ChatOpenAI
from langchain.tools import BaseTool, StructuredTool, Tool, tool
from langchain.tools import MoveFileTool, format_tool_to_openai_function
from langchain.schema import (
    AIMessage,
    HumanMessage,
    SystemMessage
)
from langchain.utilities import WikipediaAPIWrapper
from langchain.tools import AIPluginTool


# Setting up a system message for our Chatbot
#system = SystemMessage(content = "You are a helpful AI assistant") # that translates English to Pirate English.")

# driver
def predict(user_input, chatbot):

    print(f"chatbot - {chatbot}")
    print(f"user_input - {user_input}")

    chat = ChatOpenAI(
    #openai_api_key=openai_api_key,
    temperature=1.0, #temperature, #1.0
    streaming=True,
    model='gpt-3.5-turbo-0613')
    #messages = [system]
    messages=[]
    #function_call_decision = True if any(plugins) else False

    if len(chatbot) != 0:
        for conv in chatbot:
            human = HumanMessage(content=conv[0])
            ai = AIMessage(content=conv[1])
            messages.append(human)
            messages.append(ai)
        messages.append(HumanMessage(content=user_input))
        print(f"messages list is - {messages}")

    else: # for first user message
        messages.append(HumanMessage(content=user_input))
        print(f"messages list is - {messages}")

    # getting gpt3.5's response
    gpt_response = chat(messages)
    print(f"gpt_response - {gpt_response}")
    bot_message = gpt_response.content
    print(f"bot_message - {bot_message}")

    chatbot.append((user_input, bot_message))

    #return "", chatbot, None #"", chatbot
    return bot_message

chatbot = gr.Chatbot()
gr.ChatInterface(predict, chatbot=chatbot, delete_last_btn="del").queue().launch(share=False, debug=True) #examples=["How are you?", "What's up?"],