1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143 | import customtkinter
import tkinter
import pandas as pd
import os
import pickle
from .modeltrainer import preprocess
import tkinter.messagebox
import joblib
class Predict(customtkinter.CTkToplevel):
model_dir = "models/"
def __init__(self, parent, selected_model):
super().__init__(parent)
self.parent = parent
self.title("Predictions")
self.grab_set()
self.focus_set()
self.selected_model = selected_model
posx = int(self.winfo_screenwidth()/2 - 150)
posy = int(self.winfo_screenheight()/2 - 350)
self.geometry("300x650+{}+{}".format(posx, posy))
self.resizable(False, False)
# title
self.title_label = customtkinter.CTkLabel(self, text="Title", anchor='w')
self.title_label.pack(pady=5, padx=30, fill='x', side=tkinter.TOP, anchor='w')
self.title_entry = customtkinter.CTkEntry(self, width=240)
self.title_entry.pack(pady=10, padx=10)
# uncomment the following lines to add title
self.title_entry.insert(0, 'A good openCV tutorial?')
# selftext
self.selftext_label = customtkinter.CTkLabel(self, text="Selftext", anchor='w')
self.selftext_label.pack(pady=5, padx=30, fill='x', side=tkinter.TOP, anchor='w')
self.selftext_entry = customtkinter.CTkTextbox(self, width=240, height=100)
self.selftext_entry.pack(pady=10, padx=10)
# uncomment the following lines to add selftext
self.selftext_entry.insert("0.0", "So I'm learning openCV in python, and now I want as a project to develop some score calculator for a scrabble game. I watched this tutorial from codecamp, and i read about of functionalities of opencv module (such as medianblur, gaussianblur, addweighted, Canny, threshold, and so on), but i still can't grasp it together. Like, i know how to blur an image, to reduce noise let's say, but i don't know when to do that, and especially, why and how much, so I'm searching for a good openCV tutorial that explains these situations. \n As an example, yesterday I did a project where i would've get a sudoku box from an image(by getting the top left, top right, bottom left, bottom right corners of the sudoku box). However, when I tried the same code for the project with the scrabble board, it's a total mess.")
# subreddit
self.subreddit_label = customtkinter.CTkLabel(self, text="Subreddit", anchor='w')
self.subreddit_label.pack(pady=5, padx=30, fill='x', side=tkinter.TOP, anchor='w')
self.subreddit_entry = customtkinter.CTkOptionMenu(self, values=self.parent.posts['subreddit'].unique(), width=240)
self.subreddit_entry.pack(pady=10, padx=10)
self.subreddit_entry.set(self.parent.posts['subreddit'].unique()[0])
# day
self.day_label = customtkinter.CTkLabel(self, text="Day", anchor='w')
self.day_label.pack(pady=5, padx=30, fill='x', side=tkinter.TOP, anchor='w')
self.day_entry = customtkinter.CTkOptionMenu(self, values=['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'], width=240)
self.day_entry.pack(pady=10, padx=10)
self.day_entry.set('Monday')
# hour
self.hour_label = customtkinter.CTkLabel(self, text="Hour", anchor='w')
self.hour_label.pack(pady=5, padx=30, fill='x', side=tkinter.TOP, anchor='w')
hours = ['{}:00'.format(i) for i in range(24)]
self.hour_entry = customtkinter.CTkOptionMenu(self, values=hours, width=240)
self.hour_entry.pack(pady=10, padx=10)
self.hour_entry.set('10:00')
# distinguished
self.distinguished_entry = customtkinter.CTkCheckBox(self, text="Distinguished")
self.distinguished_entry.pack(pady=10, padx=10)
# button for predicting
self.predict_button = customtkinter.CTkButton(self, text="Predict", command=self.predict)
self.predict_button.pack(pady=10, padx=10)
# button for closing the window
self.close_button = customtkinter.CTkButton(self, text="Close", command=self.destroy)
self.close_button.pack(pady=10, padx=10)
def predict(self):
title = self.title_entry.get()
selftext = self.selftext_entry.get("1.0", tkinter.END)
subreddit = self.subreddit_entry.get()
day = self.day_entry.get()
hour = self.hour_entry.get()
hour = int(hour.split(':')[0])
distinguished = False if self.distinguished_entry.get() == 0 else True
if not title or not selftext:
tkinter.messagebox.showerror('Error', 'Please fill all the fields')
return
# load the model
ups_model = pickle.load(open(os.path.join(self.model_dir, self.selected_model + '_ups.pkl'), 'rb'))
num_comments_model = pickle.load(open(os.path.join(self.model_dir, self.selected_model + '_num_comments.pkl'), 'rb'))
# load the vectorizer
vectorizer = joblib.load(os.path.join(self.model_dir, "vectorizer.pkl"))
text = title + " " + selftext
text = preprocess(text)
input_data = pd.DataFrame(columns=['text', 'day', 'hour', 'subreddit', 'distinguished'])
input_data = input_data.append({'text': text, 'day': day, 'hour': hour, 'subreddit': subreddit, 'distinguished': distinguished}, ignore_index=True)
cat_cols = ['day', 'hour', 'subreddit', 'distinguished']
for col in cat_cols:
input_data[col] = input_data[col].astype('category')
input_data = pd.get_dummies(input_data, columns=cat_cols)
input_data['text'] = input_data['text'].astype(str)
X = vectorizer.transform(input_data['text'])
# predict the ups
ups = ups_model.predict(X)
ups = int(ups[0])
# predict the num_comments
num_comments = num_comments_model.predict(X)
num_comments = int(num_comments[0])
# Bias for day, hour, subreddit, distinguished, if the user does not post on best time of the day
best_time_df = self.parent.posts[['subreddit', 'day', 'hour', 'ups', 'num_comments']].groupby(['subreddit', 'day', 'hour']).sum().reset_index()
ups_best_time = best_time_df[(best_time_df['subreddit'] == subreddit) & (best_time_df['day'] == day) & (best_time_df['hour'] == hour)]['ups'].values[0]
num_comments_best_time = best_time_df[(best_time_df['subreddit'] == subreddit) & (best_time_df['day'] == day) & (best_time_df['hour'] == hour)]['num_comments'].values[0]
ups_bias = ups_best_time / ups
num_comments_bias = num_comments_best_time / num_comments
ups = ups * ups_bias
num_comments = num_comments * num_comments_bias
# distinguished bias
if distinguished:
ups = ups * 1.5
num_comments = num_comments * 1.5
ups = int(ups)
num_comments = int(num_comments)
tkinter.messagebox.showinfo('Result', 'Predicted ups: {}\nPredicted num_comments: {}'.format(ups, num_comments))
|
