# %%
import pyreadstat
import json
import re
import datetime

from utils import get_ollama_summary, get_ollama_embedding

# %%
df_sav, meta = pyreadstat.read_sav('./data/ZA8841_v1-0-0.sav')
print(df_sav.shape)

# %%
df_sav.head(1).to_dict(orient='records')

# %%
def safe_json(obj):
    if isinstance(obj, (datetime.datetime, datetime.date)):
        return obj.isoformat()
    if isinstance(obj, set):
        return list(obj)
    if hasattr(obj, '__dict__'):
        return str(obj)
    return obj

meta_dict = vars(meta)
meta_json = json.dumps(meta_dict, default=safe_json, indent=2)

# %%
with open('./data/za8841_meta.json', 'w') as f:
    f.write(meta_json)

# %%
# Load your mapping dictionary (from JSON file or directly)
with open("./data/za8841_meta.json") as f:
    meta = json.load(f)
value_labels = meta["variable_value_labels"]

# %%
# Assume df_sav is your loaded SPSS dataframe
# For each column in the mapping, map values if the column exists in df_sav
for col, mapping in value_labels.items():
    if col in df_sav.columns:
        # Convert keys to float if needed (SPSS values often are float)
        mapping_float = {float(k): v for k, v in mapping.items()}
        df_sav[col] = df_sav[col].map(mapping_float).fillna(df_sav[col])

# Now all mapped columns have human-readable values
print(df_sav.head())

# %%
labels_map = meta["column_names_to_labels"]
def make_pandas_friendly(col):
    col = labels_map.get(col, col)
    col = re.sub(r'[.\s]+', '_', col)
    col = re.sub(r'[^0-9a-zA-Z_]', '', col)
    col = col.lower()
    col = re.sub(r'__+', '_', col)      # Replace double (or more) underscores with single
    col = col.strip('_')                # Remove leading/trailing underscores
    return col

df_sav.columns = [make_pandas_friendly(col) for col in df_sav.columns]

# %%
df_sav.head(1).to_dict(orient='records')

# %%
cols_to_select = [
    'country_code_iso_3166',
    'risks_cntry_most_exposed_to_firstly',
    'risks_pers_most_exposed_to_firstly',
    'risks_pers_most_exposed_to_number_of_mentioned_risks',
    'pot_info_sources_to_learn_about_disaster_risks_firstly',
    *df_sav.columns[404:448],  # Python is 0-indexed
    'occupation_of_respondent',
    'age_recoded_6_categories',
    'size_of_community',
    # 'social_class_self_assessment_5_cat',  # not in the data due to mapping
    'direction_things_are_going_life_personally',
    'political_discussion_local_matters',
    'political_discussion_national_matters',
    # 'left_right_placement_recoded_5_cat',  # not in the data due to mapping
    'internet_use_total',
    'gender',
    'age_education',
    'standard_of_living_last_5yrs_in_light_of_crises',
    'personal_living_conditions_in_one_years_time',
    'standard_of_living_next_5yrs'
]

# Remove columns containing certain substrings
exclude_patterns = ['2nd', 'spont', 'other']
cols_to_exclude = [col for col in df_sav.columns if any(p in col for p in exclude_patterns)]
cols_to_exclude += [
    'disaster_measures_in_hh_number_of_measures',
    'disaster_pers_experienced_past_10yrs_none',
    'pot_info_sources_to_learn_about_disaster_risks_interested_in_at_least_one_source'
]

# Add region_ and education_level_ columns
cols_to_select += [col for col in df_sav.columns if col.startswith('region_')]
cols_to_select += [col for col in df_sav.columns if col.startswith('education_level_')]

final_cols = [col for col in cols_to_select if col not in cols_to_exclude]

print(f"Final number of columns: {len(final_cols)}")
final_cols

# %%
df_model = df_sav[final_cols].copy()

# %%
# %% [markdown]
# #### Combine all columns into a single string per user
# This step creates a text representation of each user, which can be sent to an embedding model.

def row_to_string(row):
    return ' | '.join(f'{col}: {row[col]}' for col in row.index)

# Combine all columns into a single string per user (except country_code_iso_3166)
df_model['user_text'] = df_model.drop(columns=['country_code_iso_3166']).apply(row_to_string, axis=1)

df_model.head()

# %%
# Convert character columns to category BEFORE adding embedding column
for col in df_model.select_dtypes(include='object').columns:
    if col != 'user_text':
        df_model[col] = df_model[col].astype('category')

# %%
# df_model["summary"] = df_model['user_text'].apply(get_ollama_summary)
# df_model.head(1)['user_text'].apply(get_ollama_summary).to_list()
df_model["embedding"] = df_model['user_text'].apply(get_ollama_embedding)

# %%
df_model.to_csv('./data/eurobarometer_preparedness_model_data_v3.csv', index=False)