Add validation for tagging type

skit_labels/cli.py

@@ -319,12 +319,17 @@ def build_cli():
     return parser
 
 
-def upload_dataset(input_file, url, token, job_id, data_source, data_label = None):
+def upload_dataset(input_file, url, token, job_id, data_source, data_label = None, tagging_type=None):
     input_file = utils.add_data_label(input_file, data_label)
     if data_source == const.SOURCE__DB:
         fn = commands.upload_dataset_to_db
     elif data_source == const.SOURCE__LABELSTUDIO:
-        fn = commands.upload_dataset_to_labelstudio
+        if tagging_type:
+            is_valid, error = utils.validate_input_data(tagging_type, input_file)
+            if not is_valid:
+                return error, None
+
+        fn = commands.upload_dataset_to_labelstudio 
     errors, df_size = asyncio.run(
         fn(
             input_file,

skit_labels/constants.py

@@ -120,4 +120,8 @@
 FROM_NAME_INTENT = "tag"
 CHOICES = "choices"
 TAXONOMY = "taxonomy"
-VALUE = "value"
+VALUE = "value"
+
+EXPECTED_COLUMNS_MAPPING = {
+    "conversation_tagging": ['situation_id', 'situation_str', 'call']
+}

skit_labels/utils.py

@@ -10,7 +10,7 @@
 from datetime import datetime
 import pandas as pd
 from typing import Union
-
+from constants import EXPECTED_COLUMNS_MAPPING
 
 LOG_LEVELS = ["CRITICAL", "ERROR", "WARNING", "SUCCESS", "INFO", "DEBUG", "TRACE"]
 
@@ -110,3 +110,37 @@ def add_data_label(input_file: str, data_label: Optional[str] = None) -> str:
     df = df.assign(data_label=data_label)
     df.to_csv(input_file, index=False)
     return input_file
+
+
+def validate_headers(input_file, tagging_type):
+    expected_columns_mapping  = EXPECTED_COLUMNS_MAPPING
+    expected_headers = expected_columns_mapping.get(tagging_type)
+
+    df = pd.read_csv(input_file)
+    column_headers = df.columns.to_list()
+    column_headers = [header.lower() for header in column_headers]
+    column_headers = sorted(column_headers)
+    expected_headers = sorted(expected_headers)
+    logger.info(f"column_headers: {column_headers}")
+    logger.info(f"expected_headers: {expected_headers}")
+
+    is_match = column_headers == expected_headers
+    mismatch_headers = []
+    logger.info(f"Is match: {is_match}")
+
+    if not is_match:
+        mismatch_headers_set =set(column_headers).symmetric_difference(set(expected_headers))
+        mismatch_headers = list(mismatch_headers_set)
+    return is_match, mismatch_headers
+
+
+def validate_input_data(tagging_type, input_file):
+    is_valid = True
+    error = ''
+    if tagging_type == 'conversation_tagging':
+        is_match, mismatch_headers = validate_headers(input_file, tagging_type)
+        if not is_match:
+            error = f'Headers in the input file does not match the expected fields. Mismatched fields = {mismatch_headers}'
+            is_valid = False
+
+    return is_valid, error