import os
import json

import gradio as gr
from openai import OpenAI
from tenacity import retry, wait_random_exponential, stop_after_attempt

from functions_definition import get_functions, get_openai_function_tools

OPENAI_KEY = os.getenv("OPENAI_KEY")
client = OpenAI(api_key=OPENAI_KEY)


@retry(wait=wait_random_exponential(multiplier=1, max=40), stop=stop_after_attempt(3))
def chat_completion_request(messages, tools=None, tool_choice=None):
    print(f"query message {messages}")
    try:
        response = client.chat.completions.create(
            model="gpt-4o",
            messages=messages,
            tools=tools,
            tool_choice=tool_choice,
        )
        print(response.choices[0].message.content)
        return response
    except Exception as e:
        print("Unable to generate ChatCompletion response!")
        print(f"Exception: {e}")
        return e


def respond(
    message,
    history: list[tuple[str, str]],
):
    messages = [
        {
            "role": "system",
            "content": [{"type": "text", "text": "You are a helpful agent"}],
        }
    ]
    for val in history:
        if val[0]:
            messages.append(
                {"role": "user", "content": [{"type": "text", "text": val[0]}]}
            )
        if val[1]:
            messages.append(
                {"role": "assistant", "content": [{"type": "text", "text": val[1]}]}
            )

    messages.append({"role": "user", "content": [{"type": "text", "text": message}]})

    response = chat_completion_request(
        messages, tools=get_openai_function_tools(), tool_choice="auto"
    )

    response_message = response.choices[0].message
    tool_calls = response_message.tool_calls
    if tool_calls:
        available_functions = get_functions()
        messages.append(response_message)
        for tool_call in tool_calls:
            function_name = tool_call.function.name
            function_to_call = available_functions[function_name]
            function_args = json.loads(tool_call.function.arguments)
            function_response = function_to_call(
                type=function_args.get("type"),
            )
            messages.append(
                {
                    "tool_call_id": tool_call.id,
                    "role": "tool",
                    "name": function_name,
                    "content": function_response,
                }
            )
        second_response = chat_completion_request(messages)
        messages.append(
            {
                "role": "assistant",
                "content": [
                    {"type": "text", "text": second_response.choices[0].message.content}
                ],
            }
        )
        return second_response.choices[0].message.content
    messages.append(
        {
            "role": "assistant",
            "content": [{"type": "text", "text": response.choices[0].message.content}],
        }
    )
    return response.choices[0].message.content


"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = gr.ChatInterface(respond, title="Function Calling Demo")


if __name__ == "__main__":
    demo.launch()