app.py

from dotenv import load_dotenv


import gradio as gr
from gradio import ChatMessage

import json
from openai import OpenAI
from datetime import datetime
import os
import re

from termcolor import cprint
import logging
logging.basicConfig(level=logging.INFO, format='[%(asctime)s][%(name)s][%(levelname)s] - %(message)s')
log = logging.getLogger(__name__)



from omegaconf import OmegaConf
from src.tools import tools, oitools


# Load the configuration file
# ===========================================================================
# Environment variables
load_dotenv(".env", override=True) 
HF_TOKEN = os.environ.get("HF_TOKEN")
LLM_BASE_URL = os.environ.get("LLM_BASE_URL")

log.info(f"Using HF_TOKEN: {HF_TOKEN[:4]}...{HF_TOKEN[-4:]}")
log.info(f"Using LLM_BASE_URL: {LLM_BASE_URL[:15]}...")

# Configuration file
config_file = "config.yaml"
cfg = OmegaConf.load(config_file)

# OpenAI API parameters
chat_params = cfg.openai.chat_params
client = OpenAI(
    base_url=f"{LLM_BASE_URL}",
    api_key=HF_TOKEN
)
logging.info(f"Client initialized: {client}")
# ===========================================================================


def today_date():
    return datetime.today().strftime('%A, %B %d, %Y, %I:%M %p')


def clean_json_string(json_str):
    return re.sub(r'[ ,}\s]+$', '', json_str) + '}'


def completion(history, model, system_prompt: str, tools=None, chat_params=chat_params):
    messages = [{"role": "system", "content": system_prompt.format(date=today_date())}]
    for msg in history:
        if isinstance(msg, dict):  
            msg = ChatMessage(**msg)
        if msg.role == "assistant" and hasattr(msg, "metadata") and msg.metadata:  
            tools_calls = json.loads(msg.metadata.get("title", "[]")) 
            messages.append({"role": "assistant", "tool_calls": tools_calls, "content": ""})
            messages.append({"role": "tool", "content": msg.content})
        else:
            messages.append({"role": msg.role, "content": msg.content})
    
    request_params = {
        "model": model,
        "messages": messages,
        **chat_params
    }
    if tools:
        request_params.update({"tool_choice": "auto", "tools": tools})

    return client.chat.completions.create(**request_params)  


def llm_in_loop(history, system_prompt, recursive):  

    try:   
        models = client.models.list()
        model = models.data[0].id
    except Exception as err:
        gr.Warning("The model is initializing. Please wait; this may take 5 to 10 minutes ⏳.", duration=20)
        raise err
    
    arguments = ""
    name = ""
    chat_completion = completion(history=history, tools=oitools, model=model, system_prompt=system_prompt)  
    appended = False


    for chunk in chat_completion:
        if chunk.choices and chunk.choices[0].delta.tool_calls:
            call = chunk.choices[0].delta.tool_calls[0]
            if hasattr(call.function, "name") and call.function.name:
                name = call.function.name
            if hasattr(call.function, "arguments") and call.function.arguments:
                arguments += call.function.arguments

        elif chunk.choices[0].delta.content:
            if not appended:
                history.append(ChatMessage(role="assistant", content=""))
                appended = True
            history[-1].content += chunk.choices[0].delta.content
            yield history[recursive:]
    
    # Convert arguments to a valid JSON
    arguments = clean_json_string(arguments) if arguments else "{}"
    arguments = json.loads(arguments)


    if appended:
        recursive -= 1
    if name:
        try:
            result = str(tools[name].invoke(input=arguments))

        except Exception as err:
            result = f"💥 Error: {err}"

        history.append(ChatMessage(
            role="assistant", 
            content=result, 
            metadata={"title": json.dumps([{"id": "call_id", "function": {"arguments": json.dumps(arguments, ensure_ascii=False), "name": name}, "type": "function"}], ensure_ascii=False)}))
        
        yield history[recursive:]
        yield from llm_in_loop(history, system_prompt, recursive - 1)


def respond(message, history, additional_inputs):  
    
    history.append(ChatMessage(role="user", content=message))
    yield from llm_in_loop(history, additional_inputs, -1)



if __name__ == "__main__":

    # system_prompt = gr.State(value=cfg.system_prompt_template)
    system_prompt = gr.Textbox(label="System prompt", value=cfg.system_prompt_template, lines=10, visible=False)
    demo = gr.ChatInterface(respond, type="messages", additional_inputs=[system_prompt])
    demo.launch()