376 lines
16 KiB
Python
376 lines
16 KiB
Python
TEST_MODE = True
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from audioop import avg
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from re import sub
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from time import sleep
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from typing import Tuple
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from numpy import average
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import praw
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from praw.models import Comment, Submission
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from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
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from psaw import PushshiftAPI
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from os.path import exists, join, realpath, split
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from RDramaAPIInterface import RDramaAPIInterface
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BANNED_WORDS_IN_POST = ['comment', 'promotion']
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def get_real_filename(filename : str):
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path_to_script = realpath(__file__)
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path_to_script_directory, _ = split(path_to_script)
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return join(path_to_script_directory, filename)
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with open(get_real_filename("id")) as f:
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client_id = f.read()
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with open(get_real_filename("secret")) as f:
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client_secret = f.read()
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with open(get_real_filename("user_agent")) as f:
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user_agent = f.read()
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reddit = praw.Reddit(
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client_id=client_id,
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client_secret=client_secret,
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user_agent=user_agent
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)
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pushshift_api = PushshiftAPI(reddit)
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def contains_banned_words(sentence):
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santitized_sentence = ''.join([i for i in sentence.lower() if i in 'abcdefghijklmnopqrstuvwxyz '])
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return bool(set(BANNED_WORDS_IN_POST).intersection(santitized_sentence.split(" ")))
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def get_based_submissions(subreddit, time_frame, limit):
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subscriber_cache = {}
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submissions = []
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most_based_submission = None
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most_based_score = 0
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most_relatively_based_submission = None
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most_relatively_based_score = 0
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for submission in reddit.subreddit(subreddit).controversial(time_frame, limit=limit):
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try:
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basedness = (1-submission.upvote_ratio)*submission.num_comments
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if (submission.author.name == "AutoModerator" or contains_banned_words(submission.title)):
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continue
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if (submission.subreddit not in subscriber_cache):
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subscriber_cache[submission.subreddit] = submission.subreddit.subscribers
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relative_basedness = ((basedness/subscriber_cache[submission.subreddit]))*100000
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if (basedness > most_based_score):
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most_based_score = basedness
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most_based_submission = submission
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if (relative_basedness > most_relatively_based_score):
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most_relatively_based_score = relative_basedness
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most_relatively_based_submission = submission
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submissions.append((basedness, relative_basedness, submission))
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print(f"(B: {basedness} RB: {relative_basedness}){submission.title}")
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except:
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print(f"Error while processing {submission}")
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return submissions
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def analyze_comments(submission : 'Submission'):
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print(f"[{submission.id}]Retrieving Comments")
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comments = pushshift_api.search_comments(subreddit=submission.subreddit.display_name, link_id=submission.id)
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comment_list = list(comments)
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print(f"[{submission.id}]Creating Network")
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comment_map = {i.id:i for i in comment_list}
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child_map = {}
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for comment in comment_map.values():
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try:
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parent_id = comment.parent_id[3:]
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if (parent_id not in child_map):
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child_map[parent_id] = []
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child_map[parent_id].append(comment)
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except:
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print(f"Error matching {comment} to its parent.")
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sid_obj = SentimentIntensityAnalyzer()
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print(f"[{submission.id}]Classifying Comments")
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user_to_total_anger = {}
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redditors = {}
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ranked_comments = []
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angry_comments = []
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for comment in comment_map.values():
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try:
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comment_info = {
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'comment' : comment
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}
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if (comment.body == '[deleted]' or comment.author == None):
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continue
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if ("t1" in comment.parent_id[0:2]): #Not a parent comment
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parent = comment_map[comment.parent_id[3:]]
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comment_info['parent'] = parent
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parent_score = parent.score
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if (comment.id in child_map):
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child_scores = [i.score for i in child_map[comment.id] if isinstance(i, Comment)]
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else:
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child_scores = []
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if len(child_scores) > 0: #More than one child - not sure how to handle the no child case
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average_child_score = sum(child_scores)/len(child_scores)
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if (average_child_score > 0 and parent_score > 0):
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comment_score = comment.score
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if (comment_score >= average_child_score and comment_score <= parent_score):
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pass
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else:
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basedness = average_child_score - comment_score
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ranked_comments.append((basedness, comment_info))
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else:
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#A parent comment
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comment_info['parent'] = None
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if (comment.id in child_map):
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child_scores = [i.score for i in child_map[comment.id] if isinstance(i, Comment)]
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else:
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child_scores = []
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if len(child_scores) > 0: #More than one child - not sure how to handle the no child case
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average_child_score = sum(child_scores)/len(child_scores)
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comment_score = comment.score
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if (comment_score >= average_child_score):
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pass
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else:
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basedness = average_child_score - comment_score
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ranked_comments.append((basedness, comment_info))
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# Add to angriness
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score = sid_obj.polarity_scores(comment.body)['compound']
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if score < -0.5:
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angry_comments.append((sid_obj.polarity_scores(comment.body)['compound'], comment_info))
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if comment.author not in user_to_total_anger:
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user_to_total_anger[comment.author] = 0.0
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redditors[comment.author] = {}
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redditors[comment.author]['comments'] = []
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redditors[comment.author]['angriness'] = 0
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redditors[comment.author]['author'] = comment.author
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user_to_total_anger[comment.author]+=score
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redditors[comment.author]['comments'].append((score, comment_info))
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redditors[comment.author]['angriness'] += score
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except Exception as e:
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print(f"Error while processing {comment}: {e}")
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print(f"[{submission.id}]Done")
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ranked_comments.sort(reverse=True, key= lambda a : a[0])
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angry_comments.sort(key=lambda a:a[0])
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lolcows = [(v, k) for k, v in user_to_total_anger.items()]
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lolcows.sort(key=lambda a:a[0])
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redditors_ranked = [(data['angriness'], data) for username, data in redditors.items()]
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redditors_ranked.sort(key=lambda a:a[0])
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return {
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'based' : ranked_comments,
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'angry': angry_comments,
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'lolcows': lolcows,
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'redditors': redditors_ranked
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}
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#get_based_submissions("all", "hour", 25, True)
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def generate_comment_display_section(submissions : 'Tuple[float, Submission]', section_title, detail_display, number_to_show, show_details = True, detail_func = lambda a : a, max_len = 1000 ):
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markdown_lines = []
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if len(submissions) != 0:
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markdown_lines.append(f"## {section_title}")
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for comment_info in submissions[:number_to_show]:
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attribute = comment_info[0]
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parent = comment_info[1]['parent']
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comment = comment_info[1]['comment']
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if (show_details):
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markdown_lines.append(f"{detail_display}: {detail_func(attribute)}")
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comment_indent = ""
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if (parent != None):
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parent_body = parent.body.replace("\n", "")
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if len(parent_body) > max_len:
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parent_body = parent_body[0:max_len-3] + "..."
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markdown_lines.append(f"> {parent_body} ({parent.score})")
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comment_indent = ">>"
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else:
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comment_indent = ">"
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comment_body = comment.body.replace("\n", "")
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if len(comment_body) > max_len:
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comment_body = comment_body[0:max_len-3] + "..."
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markdown_lines.append(f"{comment_indent} [{comment_body}](https://reddit.com{comment.permalink}) ({comment.score})")
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return markdown_lines
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def comment_basedness_score_string(basedness):
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score = 0
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if basedness > 1000:
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score = 5
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elif basedness > 500:
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score = 4
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elif basedness > 100:
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score = 3
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elif basedness > 50:
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score = 2
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elif basedness > 10:
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score = 1
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else:
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score = 0
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return get_score_string(score, "🔥", "🔘")
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def angriness_score_string(angriness):
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score = 0
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if angriness < -0.95:
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score = 5
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elif angriness < -0.9:
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score = 4
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elif angriness < -0.85:
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score = 3
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elif angriness < -0.75:
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score = 2
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elif angriness < -0.6:
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score = 1
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else:
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score = 0
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return get_score_string(score, "😡", "🔘")
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def get_reddit_full_url(partial_url):
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return f"https://reddit.com{partial_url}"
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def generate_lolcow_display_section(lolcows):
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markdown_lines = []
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biggest_lolcow_info = lolcows[0]
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biggest_lolcow_score = biggest_lolcow_info[0]
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biggest_lolcow = biggest_lolcow_info[1]['author']
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number_of_comments = len(biggest_lolcow_info[1]['comments'])
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lolcow_comments = biggest_lolcow_info[1]['comments']
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lolcow_score_string = get_score_string(-1*biggest_lolcow_score, "🐮", "🔘", allow_over=True)
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markdown_lines.append(f"# Biggest Lolcow: /u/{biggest_lolcow.name}")
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markdown_lines.append(f"Score: {lolcow_score_string}")
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markdown_lines.append(f"Number of comments: {number_of_comments}")
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comment_angryness_scores = [-1*a[0] for a in biggest_lolcow_info[1]['comments']]
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average_angriness = average(comment_angryness_scores)
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maximum_angry_info = max(lolcow_comments, key=lambda a : a[0])
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minimum_angry_info = min(lolcow_comments, key=lambda a : a[0])
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markdown_lines.append(f"Average angriness: {angriness_score_string(average_angriness)}")
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markdown_lines.append(f"Maximum angriness: [{angriness_score_string(-1*maximum_angry_info[0])}]({get_reddit_full_url(maximum_angry_info[1]['comment'].permalink)})")
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markdown_lines.append(f"Minimum angriness: [{angriness_score_string(-1*minimum_angry_info[0])}]({get_reddit_full_url(minimum_angry_info[1]['comment'].permalink)})")
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return markdown_lines
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def generate_submission_report(submission : 'Submission', absolute: bool):
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markdown_lines = []
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comment_analysis_results = analyze_comments(submission)
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basedness_display_func = lambda a : get_comment_basedness_out_of_five(a, absolute)
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markdown_lines.extend(generate_comment_display_section(comment_analysis_results['based'], "Most Based Comments", "Basedness", 3, detail_func=basedness_display_func))
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markdown_lines.extend(generate_comment_display_section(comment_analysis_results['angry'], "Angriest Comments", "Angriness", 3, detail_func=angriness_score_string))
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markdown_lines.extend(generate_lolcow_display_section(comment_analysis_results['redditors']))
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markdown_lines.append(f"")
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markdown_lines.append("*:marppy: autodrama: automating away the jobs of dramautists. :marseycapitalistmanlet: Ping HeyMoon if there are any problems or you have a suggestion :marseyjamming:*")
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return "\n\n".join(markdown_lines)
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def create_file_report(submission : 'Submission'):
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submission_name = submission.title
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print(f"Generating submission for https://reddit.com{submission.permalink}")
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filename = "".join([i.lower() for i in submission_name if i.lower() in "abcdefghijklmnopqrstuvwxyz "])[:30].replace(" ", "_") + "_" + submission.subreddit.name + ".md"
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submission_report = generate_submission_report(submission)
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print(submission_report)
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with open(filename, "wb") as f:
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f.write(submission_report.encode("utf-8"))
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def create_file_reports_for_list_of_submissions(submissions : 'list[Tuple[float, float, Submission]]'):
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for i in submissions:
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try:
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submission = i[2]
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create_file_report(submission)
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except Exception as e:
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print(f"Yikes, had a bit of a fucky wucky: {e}")
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def get_basedness_score_out_of_five(basedness : int) -> int:
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if basedness > 10000:
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return 5
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elif basedness > 5000:
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return 4
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elif basedness > 1000:
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return 3
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elif basedness > 100:
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return 2
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elif basedness > 10:
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return 1
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else:
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return 0
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def get_comment_basedness_out_of_five(basedness: int, absolute : bool):
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if (absolute):
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if basedness > 1000:
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score = 5
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elif basedness > 500:
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score = 4
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elif basedness > 100:
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score = 3
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elif basedness > 50:
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score = 2
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elif basedness > 10:
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score = 1
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else:
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score = 0
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else:
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if basedness > 100:
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score = 5
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elif basedness > 50:
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score = 4
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elif basedness > 10:
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score = 3
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elif basedness > 5:
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score = 2
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elif basedness > 1:
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score = 1
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else:
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score = 0
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return get_score_string(score, "🔥", "🔘")
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def get_score_string(score: int, filled_emoji, empty_emoji, allow_over = False) -> str:
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to_return = "".join([filled_emoji if ((i+1) <= score) else empty_emoji for i in range(5)])
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if (allow_over):
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if (score > 5):
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to_return += f"(+{int(score)-5}{filled_emoji})"
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return to_return
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def create_rdrama_report(rdrama : RDramaAPIInterface, submission : 'Submission', basedness: int, absolute_basedness: bool):
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score = get_basedness_score_out_of_five(basedness)
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score_string = get_score_string(score, "🔥" if absolute_basedness else "🤓", "🔘")
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title = f"[{score_string}] {submission.title}"
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url = f"https://reddit.com{submission.permalink}"
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body = generate_submission_report(submission, absolute_basedness)
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if len(body) > 20000:
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body = body[0:19997] + "..."
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try:
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rdrama.make_post(title, url, body)
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except Exception as e:
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print(f"Yikes, a fucky wucky occured! {e}")
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def get_first_unposted(rdrama : RDramaAPIInterface, submissions : 'list[Submission]'):
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for submission in submissions:
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if (not rdrama.has_url_been_posted(f"https://www.reddit.com{submission.permalink}")):
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return submission
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return None
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def daily_drama_post(rdrama : RDramaAPIInterface):
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print("Performing Daily Drama Post!")
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based_submissions = get_based_submissions("all", "day", 150)
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print("Posting the most relatively based submission for the day...")
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based_submissions.sort(reverse=True, key = lambda a : a[1]) #Sort by relative basedness
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most_relatively_based_submission = get_first_unposted(rdrama, [i[2] for i in based_submissions])
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create_rdrama_report(rdrama, most_relatively_based_submission, based_submissions[0][1], False)
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print("Posting the most based submission for the day...")
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based_submissions.sort(reverse=True, key = lambda a : a[0]) #Sort by basedness
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most_absolutely_based_submission = get_first_unposted(rdrama, [i[2] for i in based_submissions])
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create_rdrama_report(rdrama, most_absolutely_based_submission, based_submissions[0][0], True)
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print("Done!")
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TEST_AUTH_TOKEN = "jU_k7alzoqfogYqQgcPJ3vIWILiDtI7UWdMTmKbvuttMih-YbhRCs8B3BBCRSKkdSJ0w_JfzJn2YBkdDEw5DIf3UXb3vGTRvLB_9BQ9zBiTz9opp3MFGSudH_s_C7keq" #todo - parameterize
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if TEST_MODE:
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website = "localhost"
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auth = TEST_AUTH_TOKEN
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https = False
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timeout = 1
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else:
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website = "rdrama.net"
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with open(get_real_filename("rdrama_auth_token"), "r") as f:
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auth = f.read()
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https = True
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timeout = 10
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rdrama = RDramaAPIInterface(auth, website, timeout, https=https)
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#daily_drama_post(rdrama)
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print(generate_submission_report(Submission(reddit, "uesnfd"), True)) |