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."})
    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})
    print(messages)
    # 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
                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()