File size: 4,540 Bytes
7cb9547
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
import os
from yattag import Doc
## --------------------------------- ###
###        reading: info.txt         ###
### -------------------------------- ###
# placeholders in case info.txt does not exist
def get_article(acc, most_imp_feat):
  filename = "info.txt"
  placeholder =  "please create an info.txt to customize this text"
  note = "**Note that model accuracy is based on the uploaded data.csv and reflects how well the AI model can give correct recommendations for that dataset. An accuracy of 50% means that half of the model's predictions for that dataset were accurate. Model accuracy and most important feature can be helpful for understanding how the model works, but should not be considered absolute facts about the real world."
  
  title = bkgd = data_collection = priv_cons = bias_cons = img_src = membs = description = placeholder
  # check if info.txt is present
  if os.path.isfile(filename):
      # open info.txt in read mode
      info = open(filename, "r")
  
      # read each line to a string
      description = "An AI project created by " + info.readline()
      title = info.readline()
      bkgd = info.readline()
      data_collection = info.readline()
      priv_cons = info.readline()
      bias_cons = info.readline()
      img_src = info.readline()
      membs = info.readline()
      
      # close file
      info.close()
      
  # use yattag library to generate html
  doc, tag, text, line = Doc().ttl()
  # create html based on info.txt
  with tag('div'):
    with tag('div', klass='box model-container'):
      with tag('div', klass='spacer'):
        with tag('div', klass='box model-div'):
          line('h2', "Model Accuracy", klass='acc')
          line('p', acc)
        with tag('div', klass='box model-div'):
          line('h2', "Most Important Feature", klass='feat')
          line('p', most_imp_feat)
      with tag('div', klass='spacer'):
        line('p', note)
    with tag('div', klass='box'):
      line('h2', 'Problem Statement and Research Summary', klass='prj')
      line('p', bkgd)
    with tag('div', klass='box'):
      line('h2', 'Data Collection Plan', klass='data')
      line('p', data_collection)
    with tag('div', klass='box'):
      line('h2', 'Ethical Considerations (Data Privacy and Bias)', klass='ethics')
      with tag('ul'):
        line('li', priv_cons)
        line('li', bias_cons)
    with tag('div', klass='box'):
      line('h2', 'Our Team', klass='team')
      line('p', membs)
      doc.stag('img', src=img_src)
      
  css = '''
  .box {
    border: 2px solid black;
    text-align: center;
    margin: 10px;
    padding: 5%;
  }
  ul {
    display: inline-block;
    text-align: left;
  }
  img {
    display: block;
    margin: auto;
  }
  .description {
    text-align: center;
  }
  .panel_button {
    display: block !important;
    width: 100% !important;
    background-color: #00EACD !important;
    color: #000;
    transition: all .2s ease-out 0s !important; 
    box-shadow: 0 10px #00AEAB !important;
    border-radius: 10px !important;
  }
  .panel_button:hover {
    box-shadow: 0 5px #00AEAB;
    transform: translateY(5px);
  }
  .submit {
    color: black !important;
  }
  .selected {
    background-color: #656bd6 !important;
  }
  .radio_item {
    border-radius: 10px;
    padding-left: 10px !important;
    padding-right: 10px !important;
  }
  .radio_item:hover {
    color: #656bd6 !important;
  }
  .title {
    background-image: url(https://media.giphy.com/media/26BROrSHlmyzzHf3i/giphy.gif);
    background-size: cover;
    color: transparent;
    -moz-background-clip: text;
    -webkit-background-clip: text;
    text-transform: uppercase;
    font-size: 60px;
    line-height: .75;
    margin: 10px 0;
  }
  .panel_header {
    color: black !important;
  }
  input {
    background-color: #efeffa !important;
  }
  .acc, .feat {
    background-color: #FF3399 !important
  }
  .prj {
    background-color: #FFCE3B !important;
  }
  .data {
    background-color: #ED6800 !important;
  }
  .ethics {
    background-color: #3EE6F9 !important;
  }
  .team {
    background-color: #9581EF !important; 
  }
  .model-container {
    display: flex;
    flex-direction: column;
    justify-content: center;
  }
  .spacer {
    display: flex;
    justify-content: center;
  }
  .model-div {
    width: 45%;
  }
  @media screen and (max-width: 700px) {
    .model-container {
      flex-wrap: wrap;
    }
  }
  '''
  return {
    'article': doc.getvalue(),
    'css': css,
    'title': title,
    'description': description,
  }