app.py

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


def predict(inputs, chatbot):
    #print(inputs,chatbot,system_message)

    messages = []
    #messages.append({"role": "system", "content": system_message})
    messages.append({"role": "system", "content": "You are a discord bot called 'QuteAI', make your response like human chatting, humans do not response using lists while explaining things and don't say long sentences. Use markdown in response. Important, if user's message start with [something], start your response with q!something"})
    for conv in chatbot:
        user = conv[0]
        messages.append({"role": "user", "content": user})
        assistant = conv[1]
        messages.append({"role": "assistant", "content": assistant})
    messages.append({"role": "user", "content": inputs})

    # a ChatCompletion request
    client = openai.OpenAI(base_url="https://api.chatanywhere.tech/v1")
    completion = client.chat.completions.create(
            model="gpt-3.5-turbo",  # this field is currently unused
            messages=messages,
            temperature=0.7,
            stream=True,
        )
    new_message = {"role": "assistant", "content": ""}

    for chunk in completion:
            if chunk.choices[0].delta.content:
                print(chunk.choices[0].delta.content, end="", flush=True)
                new_message["content"] += chunk.choices[0].delta.content
                print(new_message["content"])
                yield new_message["content"]

    messages.append(new_message)
    print(messages)


interface = gr.ChatInterface(predict)
with gr.Blocks() as demo:
    gr.Markdown("""
    # GPT 3.5 Discord Bot powered by gradio!
    To use this space as a discord bot, first install the gradio_client
    
    ```bash
    pip install gradio_client
    ```
    
    Then run the following command
    
    ```python
    client = grc.Client.duplicate("gradio-discord-bots/gpt-35-turbo", private=False, secrets={"OPENAI_API_KEY": "<your-key-here>"}, sleep_timeout=2880)
    client.deploy_discord(api_names=["chat"])
    """)
    with gr.Row(visible=False):
        interface.render()

demo.queue().launch()