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The dataset generation failed because of a cast error
Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 20 new columns ({'overall_risk_score', 'hydration_level', 'alcohol_use', 'oral_hygiene', 'sleep_hours', 'smoking', 'spicy_food_intake', 'water_quality', 'autoimmune_condition', 'lifestyle_score', 'nutritional_score', 'diet_type', 'vitamin_b2_deficiency', 'iron_deficiency', 'stress_level', 'immune_status', 'hygiene_score', 'gut_disorder', 'vitamin_b12_deficiency', 'diabetes'}) and 8 missing columns ({'country', 'age_group', 'climate', 'continent', 'gender', 'income_level', 'region_type', 'age'}).

This happened while the csv dataset builder was generating data using

zip://02_risk_factors.csv::/tmp/hf-datasets-cache/medium/datasets/91354516402121-config-parquet-and-info-mahfuzmee-eng-mouth_ulcer-abdb41f4/hub/datasets--mahfuzmee-eng--mouth_ulcer_dataset/snapshots/78d8e043c1491201eec7878945d53a32b50be7c0/mouth_ulcer_dataset.zip, [/tmp/hf-datasets-cache/medium/datasets/91354516402121-config-parquet-and-info-mahfuzmee-eng-mouth_ulcer-abdb41f4/hub/datasets--mahfuzmee-eng--mouth_ulcer_dataset/snapshots/78d8e043c1491201eec7878945d53a32b50be7c0/mouth_ulcer_dataset.zip (origin=hf://datasets/mahfuzmee-eng/mouth_ulcer_dataset@78d8e043c1491201eec7878945d53a32b50be7c0/mouth_ulcer_dataset.zip)]

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1800, in _prepare_split_single
                  writer.write_table(table)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 765, in write_table
                  self._write_table(pa_table, writer_batch_size=writer_batch_size)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 773, in _write_table
                  pa_table = table_cast(pa_table, self._schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2321, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2249, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              patient_id: string
              vitamin_b2_deficiency: int64
              vitamin_b12_deficiency: int64
              iron_deficiency: int64
              stress_level: string
              sleep_hours: double
              smoking: int64
              alcohol_use: int64
              spicy_food_intake: string
              oral_hygiene: string
              immune_status: string
              diet_type: string
              diabetes: int64
              gut_disorder: int64
              autoimmune_condition: int64
              hydration_level: string
              water_quality: string
              nutritional_score: int64
              lifestyle_score: double
              hygiene_score: int64
              overall_risk_score: double
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 2918
              to
              {'patient_id': Value('string'), 'age': Value('int64'), 'age_group': Value('string'), 'gender': Value('string'), 'country': Value('string'), 'continent': Value('string'), 'region_type': Value('string'), 'income_level': Value('string'), 'climate': Value('string')}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1347, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 980, in convert_to_parquet
                  builder.download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 882, in download_and_prepare
                  self._download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 943, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1646, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1802, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 20 new columns ({'overall_risk_score', 'hydration_level', 'alcohol_use', 'oral_hygiene', 'sleep_hours', 'smoking', 'spicy_food_intake', 'water_quality', 'autoimmune_condition', 'lifestyle_score', 'nutritional_score', 'diet_type', 'vitamin_b2_deficiency', 'iron_deficiency', 'stress_level', 'immune_status', 'hygiene_score', 'gut_disorder', 'vitamin_b12_deficiency', 'diabetes'}) and 8 missing columns ({'country', 'age_group', 'climate', 'continent', 'gender', 'income_level', 'region_type', 'age'}).
              
              This happened while the csv dataset builder was generating data using
              
              zip://02_risk_factors.csv::/tmp/hf-datasets-cache/medium/datasets/91354516402121-config-parquet-and-info-mahfuzmee-eng-mouth_ulcer-abdb41f4/hub/datasets--mahfuzmee-eng--mouth_ulcer_dataset/snapshots/78d8e043c1491201eec7878945d53a32b50be7c0/mouth_ulcer_dataset.zip, [/tmp/hf-datasets-cache/medium/datasets/91354516402121-config-parquet-and-info-mahfuzmee-eng-mouth_ulcer-abdb41f4/hub/datasets--mahfuzmee-eng--mouth_ulcer_dataset/snapshots/78d8e043c1491201eec7878945d53a32b50be7c0/mouth_ulcer_dataset.zip (origin=hf://datasets/mahfuzmee-eng/mouth_ulcer_dataset@78d8e043c1491201eec7878945d53a32b50be7c0/mouth_ulcer_dataset.zip)]
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

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patient_id
string
age
int64
age_group
string
gender
string
country
string
continent
string
region_type
string
income_level
string
climate
string
P000001
38
young_adult
male
Indonesia
Asia
urban
middle
tropical
P000002
27
young_adult
female
Egypt
Africa
urban
middle
dry
P000003
41
adult
male
Brazil
South America
urban
middle
tropical
P000004
64
elderly
female
China
Asia
urban
low
temperate
P000005
26
young_adult
male
USA
North America
rural
middle
temperate
P000006
26
young_adult
female
Bangladesh
Asia
urban
high
tropical
P000007
65
elderly
female
Nigeria
Africa
rural
middle
tropical
P000008
43
adult
male
Nigeria
Africa
suburban
middle
tropical
P000009
23
young_adult
male
Japan
Asia
urban
low
temperate
P000010
39
young_adult
female
India
Asia
suburban
middle
tropical
P000011
23
young_adult
female
Pakistan
Asia
urban
middle
tropical
P000012
23
young_adult
female
India
Asia
urban
middle
tropical
P000013
33
young_adult
female
Bangladesh
Asia
urban
middle
tropical
P000014
11
child
male
Bangladesh
Asia
rural
low
tropical
P000015
12
child
male
Indonesia
Asia
urban
low
tropical
P000016
22
young_adult
male
USA
North America
urban
low
temperate
P000017
18
teen
male
Egypt
Africa
urban
middle
dry
P000018
35
young_adult
female
China
Asia
rural
low
temperate
P000019
19
teen
male
Bangladesh
Asia
rural
high
tropical
P000020
14
teen
male
India
Asia
urban
middle
tropical
P000021
62
elderly
male
USA
North America
rural
high
temperate
P000022
26
young_adult
female
Japan
Asia
urban
middle
temperate
P000023
30
young_adult
male
UK
Europe
rural
low
temperate
P000024
14
teen
female
Germany
Europe
urban
low
temperate
P000025
22
young_adult
female
India
Asia
urban
low
tropical
P000026
31
young_adult
female
Ethiopia
Africa
rural
low
tropical
P000027
16
teen
female
Pakistan
Asia
urban
middle
tropical
P000028
36
young_adult
male
Nigeria
Africa
suburban
middle
tropical
P000029
22
young_adult
male
Brazil
South America
urban
middle
tropical
P000030
25
young_adult
female
China
Asia
rural
low
temperate
P000031
22
young_adult
male
Egypt
Africa
urban
middle
dry
P000032
75
elderly
female
Egypt
Africa
urban
middle
dry
P000033
29
young_adult
female
Bangladesh
Asia
suburban
middle
tropical
P000034
17
teen
male
France
Europe
urban
low
temperate
P000035
45
adult
male
USA
North America
urban
low
temperate
P000036
16
teen
female
Australia
Oceania
urban
middle
temperate
P000037
33
young_adult
female
USA
North America
suburban
low
temperate
P000038
11
child
female
Bangladesh
Asia
urban
high
tropical
P000039
15
teen
female
France
Europe
urban
middle
temperate
P000040
33
young_adult
female
Indonesia
Asia
urban
low
tropical
P000041
43
adult
male
China
Asia
urban
middle
temperate
P000042
32
young_adult
male
UK
Europe
urban
low
temperate
P000043
28
young_adult
female
Bangladesh
Asia
suburban
middle
tropical
P000044
25
young_adult
female
India
Asia
urban
middle
tropical
P000045
14
teen
female
Egypt
Africa
urban
low
dry
P000046
20
young_adult
female
Bangladesh
Asia
rural
low
tropical
P000047
23
young_adult
male
Canada
North America
urban
middle
temperate
P000048
50
adult
female
Germany
Europe
suburban
high
temperate
P000049
35
young_adult
male
Australia
Oceania
rural
low
temperate
P000050
12
child
male
Philippines
Asia
urban
high
tropical
P000051
35
young_adult
female
China
Asia
urban
middle
temperate
P000052
24
young_adult
male
France
Europe
rural
high
temperate
P000053
21
young_adult
male
UK
Europe
rural
low
temperate
P000054
40
adult
female
India
Asia
urban
low
tropical
P000055
50
adult
female
Bangladesh
Asia
urban
low
tropical
P000056
47
adult
female
Indonesia
Asia
suburban
middle
tropical
P000057
19
teen
female
Brazil
South America
suburban
low
tropical
P000058
25
young_adult
male
Egypt
Africa
rural
middle
dry
P000059
35
young_adult
male
Ethiopia
Africa
urban
middle
tropical
P000060
48
adult
female
UK
Europe
urban
middle
temperate
P000061
23
young_adult
female
Nigeria
Africa
urban
middle
tropical
P000062
27
young_adult
female
Indonesia
Asia
rural
middle
tropical
P000063
17
teen
female
Mexico
North America
urban
middle
tropical
P000064
16
teen
male
Argentina
South America
urban
middle
temperate
P000065
44
adult
female
Bangladesh
Asia
urban
low
tropical
P000066
59
adult
male
Germany
Europe
suburban
low
temperate
P000067
28
young_adult
female
Indonesia
Asia
urban
middle
tropical
P000068
49
adult
male
China
Asia
urban
low
temperate
P000069
35
young_adult
male
India
Asia
urban
high
tropical
P000070
21
young_adult
male
USA
North America
urban
low
temperate
P000071
35
young_adult
female
Bangladesh
Asia
rural
middle
tropical
P000072
64
elderly
female
Australia
Oceania
suburban
middle
temperate
P000073
29
young_adult
male
China
Asia
urban
low
temperate
P000074
65
elderly
male
Mexico
North America
urban
high
tropical
P000075
8
child
male
Bangladesh
Asia
urban
low
tropical
P000076
45
adult
female
Pakistan
Asia
urban
middle
tropical
P000077
31
young_adult
female
India
Asia
rural
high
tropical
P000078
25
young_adult
male
China
Asia
suburban
low
temperate
P000079
31
young_adult
male
Bangladesh
Asia
urban
middle
tropical
P000080
11
child
female
Nigeria
Africa
suburban
middle
tropical
P000081
26
young_adult
female
Bangladesh
Asia
rural
middle
tropical
P000082
35
young_adult
male
USA
North America
rural
middle
temperate
P000083
62
elderly
female
Philippines
Asia
urban
middle
tropical
P000084
23
young_adult
female
Germany
Europe
urban
low
temperate
P000085
20
young_adult
male
Canada
North America
urban
high
temperate
P000086
23
young_adult
male
China
Asia
urban
middle
temperate
P000087
47
adult
male
China
Asia
suburban
middle
temperate
P000088
35
young_adult
male
France
Europe
rural
middle
temperate
P000089
22
young_adult
female
Philippines
Asia
urban
high
tropical
P000090
38
young_adult
female
Pakistan
Asia
rural
middle
tropical
P000091
31
young_adult
female
India
Asia
suburban
middle
tropical
P000092
48
adult
male
USA
North America
urban
middle
temperate
P000093
21
young_adult
male
Bangladesh
Asia
rural
low
tropical
P000094
25
young_adult
female
Ethiopia
Africa
urban
low
tropical
P000095
24
young_adult
female
China
Asia
urban
high
temperate
P000096
14
teen
male
Nigeria
Africa
urban
middle
tropical
P000097
34
young_adult
male
India
Asia
suburban
low
tropical
P000098
34
young_adult
female
UK
Europe
urban
high
temperate
P000099
30
young_adult
male
UK
Europe
suburban
middle
temperate
P000100
26
young_adult
male
Germany
Europe
rural
high
temperate
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