MultiModalData class improvement

Now csv files with text and table data can be read just in one motion

- from_csv method added
- text fields are defined automatically
- tests are added

examples/advanced/multimodal_text_num_example.py

@@ -2,45 +2,17 @@
 
 from fedot.api.main import Fedot
 
-from fedot.core.data.data import InputData
 from fedot.core.data.data_split import train_test_data_setup
 from fedot.core.data.multi_modal import MultiModalData
-from fedot.core.repository.dataset_types import DataTypesEnum
 from fedot.core.repository.tasks import Task, TaskTypesEnum
 from fedot.core.utils import fedot_project_root
 
 
-def prepare_multi_modal_data(files_path: str, task: Task) -> MultiModalData:
-    """
-    Imports data from 2 different sources (table and text)
-
-    :param files_path: path to data
-    :param task: task to solve
-    :return: MultiModalData object which contains table and text data
-    """
-
-    path = os.path.join(str(fedot_project_root()), files_path)
-
-    # import of table data
-    path_table = os.path.join(path, 'multimodal_wine_table.csv')
-    data_num = InputData.from_csv(path_table, task=task, target_columns='variety')
-
-    # import of text data
-    path_text = os.path.join(path, 'multimodal_wine_text.csv')
-    data_text = InputData.from_csv(path_text, data_type=DataTypesEnum.text, task=task, target_columns='variety')
-
-    data = MultiModalData({
-        'data_source_table': data_num,
-        'data_source_text': data_text
-    })
-
-    return data
-
-
 def run_multi_modal_example(files_path: str, is_visualise=True) -> float:
     task = Task(TaskTypesEnum.classification)
 
-    data = prepare_multi_modal_data(files_path, task)
+    path = os.path.join(str(fedot_project_root()), files_path, 'multimodal_wine.csv')
+    data = MultiModalData.from_csv(file_path=path, task=task, target_columns='variety', index_col=None)
     fit_data, predict_data = train_test_data_setup(data, shuffle_flag=True, split_ratio=0.7)
 
     automl_model = Fedot(problem='classification', timeout=10)