Husk2

.vscode/launch.json

@@ -0,0 +1,25 @@
+{
+    // Use IntelliSense to learn about possible attributes.
+    // Hover to view descriptions of existing attributes.
+    // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
+    "version": "0.2.0",
+    "configurations": [
+
+        {
+            "type": "firefox",
+            "request": "launch",
+            "reAttach": true,
+            "name": "Launch index.html",
+            "file": "${workspaceFolder}/index.html"
+        },
+        {
+            "type": "node",
+            "request": "launch",
+            "name": "Launch Program",
+            "skipFiles": [
+                "<node_internals>/**"
+            ],
+            "program": "${file}"
+        }
+    ]
+}

projectEnv/app.py

@@ -68,9 +68,3 @@ def loading():
     session['form_data'] = request.form
 
     return render_template("loading.html", gifList=gifList)
-
-
-
-
-
-

projectEnv/search.py

@@ -1,4 +1,7 @@
 import re
+import praw
+from wordcloud import WordCloud
+import matplotlib.pyplot as plt
 from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
 
 def sentiment_scores(comment_body):
@@ -15,6 +18,29 @@ def sentiment_scores(comment_body):
     else:
         return 0    #neutral
 
+def regbot(comment_body): 
+    score = 0
+    good_patt = re.compile(r'gl|good|\.*\sW\.*\s+|fire', re.IGNORECASE)
+    bad_patt = re.compile(r'rl|\sbad|\soff|return', re.IGNORECASE)
+    good = good_patt.findall(comment_body)
+    bad = bad_patt.findall(comment_body)
+    good_score = len(good)
+    bad_score = len(bad)
+    score = good_score + bad_score
+    score = good_score - bad_score
+    return(score)
 
+def linreg(submission):
+    # creates  score for a post based on the linear relation 
+    # between the comments and the average number of upvotes for a QC post
+    sd = 28.6
+    m = 2.59
+    int = 5.85
+    c = m * submission.score + int
+    return(c/sd)
 
 
+
+
+
+

projectEnv/searchCode.py

@@ -40,22 +40,33 @@ def get_data_if_exist(p_key):
 def sortRating(s):
     return s.rating
 
-def getRating(comments):   
+def getRating(prpost):   
     rating = 0
-    comments.replace_more(limit=None) #replace all unloaded comment obj with loaded comments
-    for comment in comments:
-        rating += sentiment_scores(comment.body)
+    sentrating = 0
+    sentnum = 0
+    prpost.comments.replace_more(limit=None) #replace all unloaded comment obj with loaded comments
+    # rating from the comment lin reg
+    baserate = linreg(prpost)
+    for comment in prpost.comments:
+        sentrating += sentiment_scores(comment.body)
+        sentnum = sentnum + 1
+    rating = baserate * (1 + (sentrating / sentnum))
     return rating
+
+
+
 def addToPosts(p_data):
 #look for cached data
     cached= True
     rating = get_data_if_exist(p_data.id)    
     if rating == False:
         print("getting ratings", p_data.id)
-        rating = getRating(p_data.comments)
+        rating = getRating(p_data)
         cached = False
         print("not cached")
     #add to processed post
+        print("not cached")
+    #add to processed post
 
     post_list.append(post("https://www.reddit.com"+ p_data.permalink, 
                         rating, 
@@ -67,9 +78,7 @@ def addToPosts(p_data):
 
 def searchItem(db,prompt):
 
-
-
-    #purge old processed_ids and posts
+    # purge old processed_ids and posts
 
     post_list.clear()
     total_post_list.clear()
@@ -98,4 +107,4 @@ def loadMore():
 
 
     post_list.sort(key=sortRating, reverse = True)#sort the post obj in order of rating
-    return post_list
+    return post_list

projectEnv/searching.R

@@ -0,0 +1,40 @@
+library(car)
+library(RedditExtractoR)
+library(tidyverse)
+
+
+sexbot <- function(comment_body) {
+  good_patt <- "gl|good|\\.*\\sW\\.*\\s+|fire"
+  bad_patt <- "rl|\\sbad|\\soff|return"
+
+  good <- str_count(comment_body, regex(good_patt, ignore_case = TRUE))
+  bad <- str_count(comment_body, regex(bad_patt, ignore_case = TRUE))
+
+  score <- good - bad
+  return(score)
+}
+
+prompt <- "QC"
+subreddit <- "FashionReps"
+time_filter <- "year"  # For RedditExtractoR, this will be either "all" or "day"
+
+# Fetch posts
+posts <- find_thread_urls(subreddit, keywords = prompt, sort_by = "relevance", period = time_filter)
+post_content <- get_thread_content(posts$url)
+threads <- post_content$threads
+comments <- post_content$comments
+
+# Compute averages
+average_upvotes <- mean(threads$score)
+average_comments <- mean(posts$num_comments)
+
+# linear reg
+comxup <- lm(threads$upvotes ~ threads$comments)
+summary(comxup)
+
+
+
+
+# Print results
+print(paste("Average number of upvotes for posts: ", average_upvotes))
+print(paste("Average number of comments for posts: ", average_comments))

projectEnv/stattime.py

@@ -0,0 +1,39 @@
+# attempting to explore the relationship between the average special char score and the number of upvotes
+from search import sexbot
+from searchCode import *
+import praw
+
+
+r = praw.Reddit(
+    client_id='jknOULmDh_Xkmi5xLSpl_A',
+    client_secret='5jOGVzfdgGJgRrxS7oPZAzaBZnndEA',
+    user_agent="smol man",
+)
+
+plist = []
+listyy = []
+prompt = "QC"
+
+for i in r.subreddit("FashionReps").search(query=prompt,
+                                           sort="relevance", 
+                                            limit=100, 
+                                            time_filter= "year"):
+    plist.append((i.score, i.num_comments))
+    listyy.append(getRating(i.comments))
+
+
+print(len(plist))
+# all the averaged values for 100 post
+average_upvotes = sum(upvotes for upvotes, _ in plist) / len(plist)
+average_comments = sum(comments for _, comments in plist) / len(plist)
+average_score = sum(listyy) / len(listyy)
+
+
+
+print(f"average score is {average_score}")
+print(f"Average number of upvotes for posts with '{len(plist)}' posts: {average_upvotes}")
+print(f"Average number of comments for posts with '{len(plist)}' posts: {average_comments}")
+
+
+
+

projectEnv/testing.py

@@ -0,0 +1,38 @@
+import praw
+from praw.models import MoreComments
+from flask_sqlalchemy import SQLAlchemy
+from datetime import datetime
+from praw.models.util import stream_generator
+from searchCode import *
+from search import *
+
+
+
+def searchItems(prompt):
+
+    # purge old processed_ids and posts
+
+    post_list.clear()
+    total_post_list.clear()
+    #grabs posts based on prompt
+    for i in r.subreddit("FashionReps").search(query=prompt,
+                                                    sort="relevance", 
+                                                    limit=24, 
+                                                    time_filter= "year"):
+        total_post_list.append(i)
+    #add first 6 posts
+    print("looked for posts")
+    return total_post_list
+
+postslst = searchItems(prompt='bapesta QC')
+for sub in postslst[:6]:
+    print(linreg(sub))
+
+print(postslst[3].score)
+print(postslst[3].num_comments)
+
+rating = 0
+for comment in postslst[3].comments:
+        rating += sentiment_scores(comment.body)
+
+print(rating)

projectEnv/venv/lib/python3.8/site-packages/vaderSentiment/vaderSentiment.py

@@ -684,4 +684,4 @@ def score_valence(self, sentiments, text):
                                                        str(vs['compound']), translator_name))
         print("----------------------------------------------------")
 
-    print("\n\n Demo Done!")
+    print("\n\n Demo Done!")