app.py

import gradio as gr
import os
import openai
import gradio as gr
from gradio import ChatInterface
import time

# 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

def predict_langchain(user_input, chatbot):

    print(f"Chatbot : {chatbot}")
    chat = ChatOpenAI(temperature=1.0, streaming=True, model='gpt-3.5-turbo-0613')
    messages=[]

    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))

    # getting gpt3.5's response
    gpt_response = chat(messages)
    return gpt_response.content

gr.ChatInterface(predict_langchain, delete_last_btn="del").queue().launch(share=False, debug=True)