# %%
import pandas as pd
import re

# %%
# import training file
data_path = '../esAppMod_data_import/train.csv'
train_df = pd.read_csv(data_path, skipinitialspace=True)


# import test file
data_path = '../esAppMod_data_import/test.csv'
test_df = pd.read_csv(data_path, skipinitialspace=True)

# import entity file
data_path = '../esAppMod_data_import/entity.csv'
entity_df = pd.read_csv(data_path, skipinitialspace=True)
id2label = {}
for _, row in entity_df.iterrows():
    id2label[row['id']] = row['name']


# %%
train_df
# %%

def extract_acronym_mapping(names):
    mapping = {}
    for name in names:
        # Find acronym in parentheses
        match = re.search(r"\((\w+)\)", name)
        if match:
            acronym = match.group(1)
            
            # Remove unrelated prepended terms
            core_term = re.sub(r"^([\w\s]+)\s*\(\w+\)$", r"\1", name).strip()
            
            # Add to dictionary
            mapping[acronym] = core_term
    return mapping

names = set(train_df['entity_name'].to_list())

# Extract mappings
acronym_mapping = extract_acronym_mapping(names)
print(acronym_mapping)
# %%
del acronym_mapping['E']  # too many false matches
acronym_mapping = {key.lower():value.lower() for key, value in acronym_mapping.items()}

abbrev_to_term = {rf'\b{key}\b': value for key, value in acronym_mapping.items()}
term_to_abbrev = {rf'\b{value}\b': key for key, value in acronym_mapping.items()}


# %%
abbrev_to_term
# %%
term_to_abbrev

# %%
acronym_mapping
# %%