import json
import streamlit as st

import utils.settings as settings

from crewai import Task
from crewai.tasks.task_output import TaskOutput
from pydantic import BaseModel
from typing import List

from agents.article_evaluator import article_evaluator
from tasks.create_learning_profile import learning_profile_task
from tasks.new_article_suggestion import article_suggestion_task


class EvaluatedArticle(BaseModel):
    title: str
    url: str
    evaluation_score: int
    evaluation_reason: str


class EvaluatedArticles(BaseModel):
    articles: List[EvaluatedArticle]


def callback_function(output: TaskOutput):
    evaluated_articles = json.loads(output.exported_output)['articles']

    for article in evaluated_articles:
        settings.articles[article['url']
                          ]['evaluation_score'] = article['evaluation_score']
        settings.articles[article['url']
                          ]['evaluation_reason'] = article['evaluation_reason']
    st.markdown("### Evaluate Articles task is executed successfully!")


evaluation_task = Task(
    description=(
        "Evaluate artilces based on the metric does the articles provide incremenrtal "
        "learning w.r.t the insights captured by the user. "
        "Score the articles on the scale of 1 to 10, "
        "1 being doesn't provide incremental learning and "
        "10 being provides incremental learning to the user."
        "Evaluate only articles that have been suggested to the user and no other articles."
    ),
    expected_output=(
        "List of article titles with their URLs, evaluation scores, "
        "and evaluation reasons w.r.t insights captured by the user."
    ),
    output_json=EvaluatedArticles,
    output_file="evaluated_articles.json",
    agent=article_evaluator,
    async_execution=False,
    callback=callback_function,
    context=[learning_profile_task, article_suggestion_task]
)