import json

import openai
import os
from dotenv import load_dotenv

load_dotenv()
openai.api_key = os.getenv("OPENAI_API_KEY")

chat_config = {'max_tokens': 1000, 'temperature': 0.2}
section_keys = ['reading', 'listening', 'writing', 'speaking', 'level']
grade_top_limit = 9

tools = [{
    "type": "function",
    "function": {
        "name": "save_evaluation_and_suggestions",
        "description": "Saves the evaluation and suggestions requested by input.",
        "parameters": {
            "type": "object",
            "properties": {
                "evaluation": {
                    "type": "string",
                    "description": "A comment on the IELTS section grade obtained in the specific section and what it could mean without suggestions.",
                },
                "suggestions": {
                    "type": "string",
                    "description": "A small paragraph text with suggestions on how to possibly get a better grade than the one obtained.",
                },
            },
            "required": ["evaluation", "suggestions"],
        },
    }
}]


def calculate_grading_summary(body):
    extracted_sections = extract_existing_sections_from_body(body, section_keys)

    ret = []

    for section in extracted_sections:
        openai_response_dict = calculate_section_grade_summary(section)
        ret = ret + [{'code': section['code'], 'name': section['name'], 'grade': section['grade'],
                      'evaluation': openai_response_dict['evaluation'],
                      'suggestions': openai_response_dict['suggestions']}]

    return {'sections': ret}


def calculate_section_grade_summary(section):
    res = openai.ChatCompletion.create(
        model="gpt-3.5-turbo",
        max_tokens=chat_config['max_tokens'],
        temperature=chat_config['temperature'],
        tools=tools,
        messages=[
            {
                "role": "user",
                "content": "You are a IELTS test section grade evaluator. You will receive a IELTS test section name and the grade obtained in the section. You should offer a comment on this grade with also suggestions on how to possibly get a better grade.",
            },
            {
                "role": "user",
                "content": "Section: " + str(section['name']) + " Grade: " + str(section['grade']),
            },
            {"role": "user", "content": "Speak in third person."},
            {"role": "user", "content": "Please save the evaluation and suggestions generated."}
        ])

    return parse_openai_response(res)


def parse_openai_response(response):
    if 'choices' in response and len(response['choices']) > 0 and 'message' in response['choices'][
        0] and 'tool_calls' in response['choices'][0]['message'] and isinstance(
        response['choices'][0]['message']['tool_calls'], list) and len(
        response['choices'][0]['message']['tool_calls']) > 0 and \
            response['choices'][0]['message']['tool_calls'][0]['function']['arguments']:
        return json.loads(response['choices'][0]['message']['tool_calls'][0]['function']['arguments'])
    else:
        return {'evaluation': "", 'suggestions': ""}


def extract_existing_sections_from_body(my_dict, keys_to_extract):
    if 'sections' in my_dict and isinstance(my_dict['sections'], list) and len(my_dict['sections']) > 0:
        return list(filter(
            lambda item: 'code' in item and item['code'] in keys_to_extract and 'grade' in item and 'name' in item,
            my_dict['sections']))