import os
import json
import openai

from dotenv import load_dotenv

load_dotenv()

openai.api_base = 'http://localhost:2332/v1'
openai.api_key = os.environ['NOVA_KEY']

# Example dummy function hard coded to return the same weather
# In production, this could be your backend API or an external API
def get_current_weather(location, unit='fahrenheit'):
    """Get the current weather in a given location"""
    weather_info = {
        'location': location,
        'temperature': '72',
        'unit': unit,
        'forecast': ['sunny', 'windy'],
    }
    return json.dumps(weather_info)

def run_conversation():
    # Step 1: send the conversation and available functions to GPT
    messages = [{'role': 'user', 'content': 'What\'s the weather like in Boston?'}]
    functions = [
        {
            'name': 'get_current_weather',
            'description': 'Get the current weather in a given location',
            'parameters': {
                'type': 'object',
                'properties': {
                    'location': {
                        'type': 'string',
                        'description': 'The city and state, e.g. San Francisco, CA',
                    },
                    'unit': {'type': 'string', 'enum': ['celsius', 'fahrenheit']},
                },
                'required': ['location'],
            },
        }
    ]
    response = openai.ChatCompletion.create(
        model='gpt-3.5-turbo-0613',
        messages=messages,
        functions=functions,
        function_call='auto',  # auto is default, but we'll be explicit
    )
    response_message = response['choices'][0]['message']

    # Step 2: check if GPT wanted to call a function
    if response_message.get('function_call'):
        # Step 3: call the function
        # Note: the JSON response may not always be valid; be sure to handle errors
        available_functions = {
            'get_current_weather': get_current_weather,
        }  # only one function in this example, but you can have multiple
        function_name = response_message['function_call']['name']
        fuction_to_call = available_functions[function_name]
        function_args = json.loads(response_message['function_call']['arguments'])
        function_response = fuction_to_call(
            location=function_args.get('location'),
            unit=function_args.get('unit'),
        )

        # Step 4: send the info on the function call and function response to GPT
        messages.append(response_message)  # extend conversation with assistant's reply
        messages.append(
            {
                'role': 'function',
                'name': function_name,
                'content': function_response,
            }
        )  # extend conversation with function response
        second_response = openai.ChatCompletion.create(
            model='gpt-3.5-turbo-0613',
            messages=messages,
        )  # get a new response from GPT where it can see the function response
        return second_response

print(run_conversation())