from dataclasses import dataclass
import itertools as it
from functools import reduce, partial
import datetime as dt

import pandas as pd
pd.set_option('display.max_colwidth', 150)
import praw
from praw.models import MoreComments
import matplotlib.pyplot as plt
import hfapi
import spacy
from spacytextblob.spacytextblob import SpacyTextBlob

nlp = spacy.load("en_core_web_sm")
spacy_text_blob = SpacyTextBlob()
nlp.add_pipe(spacy_text_blob)

client = hfapi.Client()

reddit = praw.Reddit("bot1") # Pulls from praw.ini file
rnba = reddit.subreddit('nba')

@dataclass
class RedditSubmission:
    title: str 
    body: str 
    permalink: str 
    author: str 
    score: float
    timestamp: dt.datetime
    
    def to_dict(self):
        return {
            'title': self.title,
            'body': self.body,
            'permalink': self.permalink,
            'author': self.author,
            'score': self.score,
            'timestamp': self.timestamp
        }
    
    @classmethod
    def from_praw_submission(
        cls,
        praw_submission: praw.models.Submission
    ):      
        return cls(
            praw_submission.title,
            praw_submission.selftext,
            praw_submission.permalink,
            praw_submission.author,
            praw_submission.score,
            dt.datetime.fromtimestamp(praw_submission.created_utc)
        )
        
@dataclass
class RedditComment:                                                                  
    body: str
    permalink: str
    author: str
    score: float
    timestamp: dt.datetime

    def to_dict(self):
        return {
            'body': self.body,
            'permalink': self.permalink,
            'author': self.author,
            'score': self.score,
            'timestamp': self.timestamp
        }

    @classmethod
    def from_praw_comment(
        cls,
        praw_comment: praw.models.Comment
    ):
        return cls(
            praw_comment.body,
            praw_comment.permalink,
            praw_comment.author,
            praw_comment.score,
            dt.datetime.fromtimestamp(praw_comment.created_utc)
        )

        
def process_submission_from_praw(praw_submission_generator):
    for praw_submission in praw_submission_generator:
        yield RedditSubmission.from_praw_submission(praw_submission)
        
def process_comment_from_praw_submission(praw_submission_generator):
    for praw_submission in praw_submission_generator:
        for praw_comment in praw_submission.comments:
            if isinstance(praw_comment, MoreComments):
                continue
            else:
                yield RedditComment.from_praw_comment(praw_comment)

praw_submission_generator1 = rnba.hot(limit=100)
praw_submission_generator2 = rnba.hot(limit=100)

submissions = process_submission_from_praw(praw_submission_generator1)
comments = process_comment_from_praw_submission(praw_submission_generator2)

submission_df = pd.DataFrame(a.to_dict() for a in submissions)
comment_df = pd.DataFrame(a.to_dict() for a in comments)

def classification_single_body(client, sentence):
    classification = client.text_classification(sentence)
    if 'error' in classification:
        return None, None
    neg_sentiment, pos_sentiment = classification[0]

    return neg_sentiment['score'], pos_sentiment['score']

def classification_multiple_body(client, bunch_of_sentences, colnames=None):
    if colnames is None:
        colnames = ['negative_score', 'positive_score']
    df = pd.DataFrame(
        map(lambda x: classification_single_body(client, x), bunch_of_sentences),
        columns=colnames
    )

    
    return df

client = hfapi.Client()
classification_multiple_bodies_partial = partial(classification_multiple_body, client)

submission_df = pd.concat([
    submission_df, classification_multiple_bodies_partial(submission_df['title'].to_list())
], axis=1)

submission_df.sort_values("negative_score")[['title', 'score', 'negative_score', 'positive_score']]

comment_df = pd.concat([
    comment_df, classification_multiple_bodies_partial(comment_df['body'].to_list())
], axis=1)

submission_df['title_sentiment'] = [*map(lambda x: x._.sentiment.polarity, nlp.pipe(submission_df['title']))]
submission_df['body_sentiment'] = [*map(lambda x: x._.sentiment.polarity, nlp.pipe(submission_df['body']))]
comment_df['body_sentiment'] = [*map(lambda x: x._.sentiment.polarity, nlp.pipe(comment_df['body']))]

submission_df[['title', 'score', 'title_sentiment']].sort_values("title_sentiment")

comment_df[['body', 'score', 'body_sentiment']].sort_values("body_sentiment")