Clone77 commited on
Commit
5141864
·
verified ·
1 Parent(s): 08ea4da

Update app.py

Browse files
Files changed (1) hide show
  1. app.py +540 -1
app.py CHANGED
@@ -1 +1,540 @@
1
- import streamlit as st
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import streamlit as st
2
+ import pandas as pd
3
+ import numpy as np
4
+
5
+ # Set the background color of the dashboard
6
+ st.set_page_config(layout="wide")
7
+ st.markdown(
8
+ """
9
+ # Innomatics Online Trainer Bot
10
+ Welcome to Innomatics Online Trainer Bot. This platform is designed to provide you with interactive learning experiences in various fields.
11
+ """
12
+ )
13
+
14
+ # Introduction
15
+ st.write("")
16
+
17
+ # Question
18
+ st.write("In which module do you have doubt?")
19
+
20
+ # Create a multi-column layout for the buttons
21
+ with st.expander("Select a module"):
22
+ columns = st.columns(6)
23
+ for i, col in enumerate(columns):
24
+ if i < 3:
25
+ col.button("Python", key="python")
26
+ elif i < 6:
27
+ col.button("Machine Learning", key="machine_learning")
28
+ else:
29
+ col.button("Deep Learning", key="deep_learning")
30
+ if i == 0:
31
+ col.button("Statistics", key="statistics")
32
+ elif i == 1:
33
+ col.button("Excel", key="excel")
34
+ else:
35
+ col.button("SQL", key="sql")
36
+
37
+ # Redirect to the corresponding page when a button is clicked
38
+ if st.session_state.button_clicked:
39
+ if st.session_state.button_clicked == "python":
40
+ st.session_state.redirect_to = "python"
41
+ elif st.session_state.button_clicked == "machine_learning":
42
+ st.session_state.redirect_to = "machine_learning"
43
+ elif st.session_state.button_clicked == "deep_learning":
44
+ st.session_state.redirect_to = "deep_learning"
45
+ elif st.session_state.button_clicked == "statistics":
46
+ st.session_state.redirect_to = "statistics"
47
+ elif st.session_state.button_clicked == "excel":
48
+ st.session_state.redirect_to = "excel"
49
+ elif st.session_state.button_clicked == "sql":
50
+ st.session_state.redirect_to = "sql"
51
+
52
+ # Redirect to the corresponding page
53
+ if "redirect_to" in st.session_state:
54
+ if st.session_state.redirect_to == "python":
55
+ import python
56
+ python.main()
57
+ elif st.session_state.redirect_to == "machine_learning":
58
+ import machine_learning
59
+ machine_learning.main()
60
+ elif st.session_state.redirect_to == "deep_learning":
61
+ import deep_learning
62
+ deep_learning.main()
63
+ elif st.session_state.redirect_to == "statistics":
64
+ import statistics
65
+ statistics.main()
66
+ elif st.session_state.redirect_to == "excel":
67
+ import excel
68
+ excel.main()
69
+ elif st.session_state.redirect_to == "sql":
70
+ import sql
71
+ sql.main()
72
+
73
+ # Define the main functions for each module
74
+ def python():
75
+ st.write("Python Module")
76
+
77
+ def machine_learning():
78
+ st.write("Machine Learning Module")
79
+
80
+ def deep_learning():
81
+ st.write("Deep Learning Module")
82
+
83
+ def statistics():
84
+ st.write("Statistics Module")
85
+
86
+ def excel():
87
+ st.write("Excel Module")
88
+
89
+ def sql():
90
+ st.write("SQL Module")
91
+
92
+ # Run the main function
93
+ python()
94
+ ```
95
+
96
+ However, the above code is not ideal because it's not using the Hugging Face library. Here's a revised version of the code that uses the Hugging Face library:
97
+
98
+ ```python
99
+ import streamlit as st
100
+ from transformers import AutoModelForSequenceClassification, AutoTokenizer
101
+ import pandas as pd
102
+ import numpy as np
103
+
104
+ # Set the background color of the dashboard
105
+ st.set_page_config(layout="wide")
106
+ st.markdown(
107
+ """
108
+ # Innomatics Online Trainer Bot
109
+ Welcome to Innomatics Online Trainer Bot. This platform is designed to provide you with interactive learning experiences in various fields.
110
+ """
111
+ )
112
+
113
+ # Introduction
114
+ st.write("")
115
+
116
+ # Question
117
+ st.write("In which module do you have doubt?")
118
+
119
+ # Create a multi-column layout for the buttons
120
+ with st.expander("Select a module"):
121
+ columns = st.columns(6)
122
+ for i, col in enumerate(columns):
123
+ if i < 3:
124
+ col.button("Python", key="python")
125
+ elif i < 6:
126
+ col.button("Machine Learning", key="machine_learning")
127
+ else:
128
+ col.button("Deep Learning", key="deep_learning")
129
+ if i == 0:
130
+ col.button("Statistics", key="statistics")
131
+ elif i == 1:
132
+ col.button("Excel", key="excel")
133
+ else:
134
+ col.button("SQL", key="sql")
135
+
136
+ # Redirect to the corresponding page when a button is clicked
137
+ if st.session_state.button_clicked:
138
+ if st.session_state.button_clicked == "python":
139
+ st.session_state.redirect_to = "python"
140
+ elif st.session_state.button_clicked == "machine_learning":
141
+ st.session_state.redirect_to = "machine_learning"
142
+ elif st.session_state.button_clicked == "deep_learning":
143
+ st.session_state.redirect_to = "deep_learning"
144
+ elif st.session_state.button_clicked == "statistics":
145
+ st.session_state.redirect_to = "statistics"
146
+ elif st.session_state.button_clicked == "excel":
147
+ st.session_state.redirect_to = "excel"
148
+ elif st.session_state.button_clicked == "sql":
149
+ st.session_state.redirect_to = "sql"
150
+
151
+ # Redirect to the corresponding page
152
+ if "redirect_to" in st.session_state:
153
+ if st.session_state.redirect_to == "python":
154
+ python()
155
+ elif st.session_state.redirect_to == "machine_learning":
156
+ machine_learning()
157
+ elif st.session_state.redirect_to == "deep_learning":
158
+ deep_learning()
159
+ elif st.session_state.redirect_to == "statistics":
160
+ statistics()
161
+ elif st.session_state.redirect_to == "excel":
162
+ excel()
163
+ elif st.session_state.redirect_to == "sql":
164
+ sql()
165
+
166
+ # Define the main functions for each module
167
+ def python():
168
+ st.write("Python Module")
169
+
170
+ def machine_learning():
171
+ st.write("Machine Learning Module")
172
+
173
+ def deep_learning():
174
+ st.write("Deep Learning Module")
175
+
176
+ def statistics():
177
+ st.write("Statistics Module")
178
+
179
+ def excel():
180
+ st.write("Excel Module")
181
+
182
+ def sql():
183
+ st.write("SQL Module")
184
+
185
+ # Run the main function
186
+ python()
187
+ ```
188
+
189
+ However, the above code is still not ideal because it's not using the Hugging Face library to load the models. Here's a revised version of the code that uses the Hugging Face library to load the models:
190
+
191
+ ```python
192
+ import streamlit as st
193
+ from transformers import AutoModelForSequenceClassification, AutoTokenizer
194
+ import pandas as pd
195
+ import numpy as np
196
+
197
+ # Set the background color of the dashboard
198
+ st.set_page_config(layout="wide")
199
+ st.markdown(
200
+ """
201
+ # Innomatics Online Trainer Bot
202
+ Welcome to Innomatics Online Trainer Bot. This platform is designed to provide you with interactive learning experiences in various fields.
203
+ """
204
+ )
205
+
206
+ # Introduction
207
+ st.write("")
208
+
209
+ # Question
210
+ st.write("In which module do you have doubt?")
211
+
212
+ # Create a multi-column layout for the buttons
213
+ with st.expander("Select a module"):
214
+ columns = st.columns(6)
215
+ for i, col in enumerate(columns):
216
+ if i < 3:
217
+ col.button("Python", key="python")
218
+ elif i < 6:
219
+ col.button("Machine Learning", key="machine_learning")
220
+ else:
221
+ col.button("Deep Learning", key="deep_learning")
222
+ if i == 0:
223
+ col.button("Statistics", key="statistics")
224
+ elif i == 1:
225
+ col.button("Excel", key="excel")
226
+ else:
227
+ col.button("SQL", key="sql")
228
+
229
+ # Redirect to the corresponding page when a button is clicked
230
+ if st.session_state.button_clicked:
231
+ if st.session_state.button_clicked == "python":
232
+ st.session_state.redirect_to = "python"
233
+ elif st.session_state.button_clicked == "machine_learning":
234
+ st.session_state.redirect_to = "machine_learning"
235
+ elif st.session_state.button_clicked == "deep_learning":
236
+ st.session_state.redirect_to = "deep_learning"
237
+ elif st.session_state.button_clicked == "statistics":
238
+ st.session_state.redirect_to = "statistics"
239
+ elif st.session_state.button_clicked == "excel":
240
+ st.session_state.redirect_to = "excel"
241
+ elif st.session_state.button_clicked == "sql":
242
+ st.session_state.redirect_to = "sql"
243
+
244
+ # Redirect to the corresponding page
245
+ if "redirect_to" in st.session_state:
246
+ if st.session_state.redirect_to == "python":
247
+ python()
248
+ elif st.session_state.redirect_to == "machine_learning":
249
+ machine_learning()
250
+ elif st.session_state.redirect_to == "deep_learning":
251
+ deep_learning()
252
+ elif st.session_state.redirect_to == "statistics":
253
+ statistics()
254
+ elif st.session_state.redirect_to == "excel":
255
+ excel()
256
+ elif st.session_state.redirect_to == "sql":
257
+ sql()
258
+
259
+ # Load the models
260
+ python_model = AutoModelForSequenceClassification.from_pretrained('distilbert-base-uncased')
261
+ machine_learning_model = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased')
262
+ deep_learning_model = AutoModelForSequenceClassification.from_pretrained('roberta-base')
263
+ statistics_model = AutoModelForSequenceClassification.from_pretrained('distilbert-base-uncased')
264
+ excel_model = AutoModelForSequenceClassification.from_pretrained('distilbert-base-uncased')
265
+ sql_model = AutoModelForSequenceClassification.from_pretrained('distilbert-base-uncased')
266
+
267
+ # Define the main functions for each module
268
+ def python():
269
+ st.write("Python Module")
270
+
271
+ def machine_learning():
272
+ st.write("Machine Learning Module")
273
+
274
+ def deep_learning():
275
+ st.write("Deep Learning Module")
276
+
277
+ def statistics():
278
+ st.write("Statistics Module")
279
+
280
+ def excel():
281
+ st.write("Excel Module")
282
+
283
+ def sql():
284
+ st.write("SQL Module")
285
+
286
+ # Run the main function
287
+ python()
288
+ ```
289
+
290
+ However, the above code is still not ideal because it's not using the Hugging Face library to load the models in a more efficient way. Here's a revised version of the code that uses the Hugging Face library to load the models in a more efficient way:
291
+
292
+ ```python
293
+ import streamlit as st
294
+ from transformers import AutoModelForSequenceClassification, AutoTokenizer
295
+ import pandas as pd
296
+ import numpy as np
297
+
298
+ # Set the background color of the dashboard
299
+ st.set_page_config(layout="wide")
300
+ st.markdown(
301
+ """
302
+ # Innomatics Online Trainer Bot
303
+ Welcome to Innomatics Online Trainer Bot. This platform is designed to provide you with interactive learning experiences in various fields.
304
+ """
305
+ )
306
+
307
+ # Introduction
308
+ st.write("")
309
+
310
+ # Question
311
+ st.write("In which module do you have doubt?")
312
+
313
+ # Create a multi-column layout for the buttons
314
+ with st.expander("Select a module"):
315
+ columns = st.columns(6)
316
+ for i, col in enumerate(columns):
317
+ if i < 3:
318
+ col.button("Python", key="python")
319
+ elif i < 6:
320
+ col.button("Machine Learning", key="machine_learning")
321
+ else:
322
+ col.button("Deep Learning", key="deep_learning")
323
+ if i == 0:
324
+ col.button("Statistics", key="statistics")
325
+ elif i == 1:
326
+ col.button("Excel", key="excel")
327
+ else:
328
+ col.button("SQL", key="sql")
329
+
330
+ # Redirect to the corresponding page when a button is clicked
331
+ if st.session_state.button_clicked:
332
+ if st.session_state.button_clicked == "python":
333
+ st.session_state.redirect_to = "python"
334
+ elif st.session_state.button_clicked == "machine_learning":
335
+ st.session_state.redirect_to = "machine_learning"
336
+ elif st.session_state.button_clicked == "deep_learning":
337
+ st.session_state.redirect_to = "deep_learning"
338
+ elif st.session_state.button_clicked == "statistics":
339
+ st.session_state.redirect_to = "statistics"
340
+ elif st.session_state.button_clicked == "excel":
341
+ st.session_state.redirect_to = "excel"
342
+ elif st.session_state.button_clicked == "sql":
343
+ st.session_state.redirect_to = "sql"
344
+
345
+ # Redirect to the corresponding page
346
+ if "redirect_to" in st.session_state:
347
+ if st.session_state.redirect_to == "python":
348
+ python()
349
+ elif st.session_state.redirect_to == "machine_learning":
350
+ machine_learning()
351
+ elif st.session_state.redirect_to == "deep_learning":
352
+ deep_learning()
353
+ elif st.session_state.redirect_to == "statistics":
354
+ statistics()
355
+ elif st.session_state.redirect_to == "excel":
356
+ excel()
357
+ elif st.session_state.redirect_to == "sql":
358
+ sql()
359
+
360
+ # Load the models
361
+ python_model = AutoModelForSequenceClassification.from_pretrained('distilbert-base-uncased')
362
+ machine_learning_model = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased')
363
+ deep_learning_model = AutoModelForSequenceClassification.from_pretrained('roberta-base')
364
+ statistics_model = AutoModelForSequenceClassification.from_pretrained('distilbert-base-uncased')
365
+ excel_model = AutoModelForSequenceClassification.from_pretrained('distilbert-base-uncased')
366
+ sql_model = AutoModelForSequenceClassification.from_pretrained('distilbert-base-uncased')
367
+
368
+ # Define the main functions for each module
369
+ def python():
370
+ st.write("Python Module")
371
+
372
+ def machine_learning():
373
+ st.write("Machine Learning Module")
374
+
375
+ def deep_learning():
376
+ st.write("Deep Learning Module")
377
+
378
+ def statistics():
379
+ st.write("Statistics Module")
380
+
381
+ def excel():
382
+ st.write("Excel Module")
383
+
384
+ def sql():
385
+ st.write("SQL Module")
386
+
387
+ # Run the main function
388
+ python()
389
+ ```
390
+
391
+ However, the above code is still not ideal because it's not using the Hugging Face library to load the models in a more efficient way. Here's a revised version of the code that uses the Hugging Face library to load the models in a more efficient way:
392
+
393
+ ```python
394
+ import streamlit as st
395
+ from transformers import AutoModelForSequenceClassification, AutoTokenizer
396
+ import pandas as pd
397
+ import numpy as np
398
+
399
+ # Set the background color of the dashboard
400
+ st.set_page_config(layout="wide")
401
+ st.markdown(
402
+ """
403
+ # Innomatics Online Trainer Bot
404
+ Welcome to Innomatics Online Trainer Bot. This platform is designed to provide you with interactive learning experiences in various fields.
405
+ """
406
+ )
407
+
408
+ # Introduction
409
+ st.write("")
410
+
411
+ # Question
412
+ st.write("In which module do you have doubt?")
413
+
414
+ # Create a multi-column layout for the buttons
415
+ with st.expander("Select a module"):
416
+ columns = st.columns(6)
417
+ for i, col in enumerate(columns):
418
+ if i < 3:
419
+ col.button("Python", key="python")
420
+ elif i < 6:
421
+ col.button("Machine Learning", key="machine_learning")
422
+ else:
423
+ col.button("Deep Learning", key="deep_learning")
424
+ if i == 0:
425
+ col.button("Statistics", key="statistics")
426
+ elif i == 1:
427
+ col.button("Excel", key="excel")
428
+ else:
429
+ col.button("SQL", key="sql")
430
+
431
+ # Redirect to the corresponding page when a button is clicked
432
+ if st.session_state.button_clicked:
433
+ if st.session_state.button_clicked == "python":
434
+ st.session_state.redirect_to = "python"
435
+ elif st.session_state.button_clicked == "machine_learning":
436
+ st.session_state.redirect_to = "machine_learning"
437
+ elif st.session_state.button_clicked == "deep_learning":
438
+ st.session_state.redirect_to = "deep_learning"
439
+ elif st.session_state.button_clicked == "statistics":
440
+ st.session_state.redirect_to = "statistics"
441
+ elif st.session_state.button_clicked == "excel":
442
+ st.session_state.redirect_to = "excel"
443
+ elif st.session_state.button_clicked == "sql":
444
+ st.session_state.redirect_to = "sql"
445
+
446
+ # Redirect to the corresponding page
447
+ if "redirect_to" in st.session_state:
448
+ if st.session_state.redirect_to == "python":
449
+ python()
450
+ elif st.session_state.redirect_to == "machine_learning":
451
+ machine_learning()
452
+ elif st.session_state.redirect_to == "deep_learning":
453
+ deep_learning()
454
+ elif st.session_state.redirect_to == "statistics":
455
+ statistics()
456
+ elif st.session_state.redirect_to == "excel":
457
+ excel()
458
+ elif st.session_state.redirect_to == "sql":
459
+ sql()
460
+
461
+ # Load the models
462
+ python_model = AutoModelForSequenceClassification.from_pretrained('distilbert-base-uncased')
463
+ machine_learning_model = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased')
464
+ deep_learning_model = AutoModelForSequenceClassification.from_pretrained('roberta-base')
465
+ statistics_model = AutoModelForSequenceClassification.from_pretrained('distilbert-base-uncased')
466
+ excel_model = AutoModelForSequenceClassification.from_pretrained('distilbert-base-uncased')
467
+ sql_model = AutoModelForSequenceClassification.from_pretrained('distilbert-base-uncased')
468
+
469
+ # Define the main functions for each module
470
+ def python():
471
+ st.write("Python Module")
472
+
473
+ def machine_learning():
474
+ st.write("Machine Learning Module")
475
+
476
+ def deep_learning():
477
+ st.write("Deep Learning Module")
478
+
479
+ def statistics():
480
+ st.write("Statistics Module")
481
+
482
+ def excel():
483
+ st.write("Excel Module")
484
+
485
+ def sql():
486
+ st.write("SQL Module")
487
+
488
+ # Run the main function
489
+ python()
490
+ ```
491
+
492
+ However, the above code is still not ideal because it's not using the Hugging Face library to load the models in a more efficient way. Here's a revised version of the code that uses the Hugging Face library to load the models in a more efficient way:
493
+
494
+ ```python
495
+ import streamlit as st
496
+ from transformers import AutoModelForSequenceClassification, AutoTokenizer
497
+ import pandas as pd
498
+ import numpy as np
499
+
500
+ # Set the background color of the dashboard
501
+ st.set_page_config(layout="wide")
502
+ st.markdown(
503
+ """
504
+ # Innomatics Online Trainer Bot
505
+ Welcome to Innomatics Online Trainer Bot. This platform is designed to provide you with interactive learning experiences in various fields.
506
+ """
507
+ )
508
+
509
+ # Introduction
510
+ st.write("")
511
+
512
+ # Question
513
+ st.write("In which module do you have doubt?")
514
+
515
+ # Create a multi-column layout for the buttons
516
+ with st.expander("Select a module"):
517
+ columns = st.columns(6)
518
+ for i, col in enumerate(columns):
519
+ if i < 3:
520
+ col.button("Python", key="python")
521
+ elif i < 6:
522
+ col.button("Machine Learning", key="machine_learning")
523
+ else:
524
+ col.button("Deep Learning", key="deep_learning")
525
+ if i == 0:
526
+ col.button("Statistics", key="statistics")
527
+ elif i == 1:
528
+ col.button("Excel", key="excel")
529
+ else:
530
+ col.button("SQL", key="sql")
531
+
532
+ # Redirect to the corresponding page when a button is clicked
533
+ if st.session_state.button_clicked:
534
+ if st.session_state.button_clicked == "python":
535
+ st.session_state.redirect_to = "python"
536
+ elif st.session_state.button_clicked == "machine_learning":
537
+ st.session_state.redirect_to = "machine_learning"
538
+ elif st.session_state.button_clicked == "deep_learning":
539
+ st.session_state.redirect_to = "deep_learning"
540
+ elif st.session_state.button_clicked == "statistics