# %%
import pandas as pd
import numpy as np
from utils import get_ollama_embedding

# %%[markdown]
#### Data Preparation Steps

# - Select relevant columns from the cleaned dataset, including a range and specific variables.
# - Exclude columns containing substrings like "2nd", "spont", "other", and some specific variables.
# - Add columns starting with "region_" and "education_level_".
# - Convert character columns to categorical type.
# - Generate a `subregion` variable by coalescing region columns, then drop the originals.
# - Generate an `education_level` variable by coalescing education columns, replacing "Not mentioned" with NA, then drop the originals.
# - Define core preparedness items and their human-readable labels.
# - Map country codes to country names.

# %%
# Load your data
data_cleaned = pd.read_csv('./data/eurobarometer_data_cleaned_csv.csv')

print(data_cleaned.shape)

# Select columns by name and range
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',
    *data_cleaned.columns[404:448],  # Python is 0-indexed
    'occupation_of_respondent',
    'age_recoded_6_categories',
    'size_of_community',
    'social_class_self_assessment_5_cat',
    'direction_things_are_going_life_personally',
    'political_discussion_local_matters',
    'political_discussion_national_matters',
    'left_right_placement_recoded_5_cat',
    '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 data_cleaned.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 data_cleaned.columns if col.startswith('region_')]
cols_to_select += [col for col in data_cleaned.columns if col.startswith('education_level_')]

final_cols = [col for col in cols_to_select if col not in cols_to_exclude]
df_model = data_cleaned[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')

# Generate embeddings for each user using Ollama
df_model['embedding'] = df_model['user_text'].apply(get_ollama_embedding)

# Add new columns to final_cols
final_cols.extend(["user_text", "embedding"])

# Check the lengths of all embeddings
embedding_lengths = df_model['embedding'].apply(lambda x: len(x) if isinstance(x, list) else None)
print('Embedding lengths:', embedding_lengths.tolist())
df_model = df_model[final_cols].copy()

# Generate subregion variable
region_cols = [col for col in df_model.columns if col.startswith('region_')]
df_model['subregion'] = df_model[region_cols].bfill(axis=1).iloc[:, 0]
df_model.drop(columns=region_cols, inplace=True)

# Generate education_level variable
edu_cols = [col for col in df_model.columns if col.startswith('education_level_')]
for col in edu_cols:
    df_model[col] = df_model[col].replace('Not mentioned', np.nan)
df_model['education_level'] = df_model[edu_cols].bfill(axis=1).iloc[:, 0]
df_model['education_level'] = df_model['education_level'].astype('category')
df_model.drop(columns=edu_cols, inplace=True)

# Core preparedness items
CORE_ITEMS_MAPPED = [
    "disaster_measures_in_hh_emergency_supply_drinks_food",
    "disaster_measures_in_hh_emergency_supply_water_cooking_hygiene", 
    "disaster_measures_in_hh_agreed_with_friends_family_to_contact",
    "disaster_measures_in_hh_discussed_common_prot_measures_in_neighbourhood",
    "disaster_measures_in_hh_battery_powered_radio"
]

CORE_ITEM_LABELS = {
    "disaster_measures_in_hh_emergency_supply_drinks_food": "Emergency supply of drinks, food",
    "disaster_measures_in_hh_emergency_supply_water_cooking_hygiene": "Emergency supply of cooking and hygiene water",
    "disaster_measures_in_hh_agreed_with_friends_family_to_contact": "Agreed with family, friends on how to contact in an emergency",
    "disaster_measures_in_hh_discussed_common_prot_measures_in_neighbourhood": "Discussed precautions in neighbourhood",
    "disaster_measures_in_hh_battery_powered_radio": "Battery-powered radio accessible"
}

COUNTRY_NAME_MAP = {
    "FI": "Finland",
    "DE-E": "East Germany",
    "DE-W": "West Germany",
    "FR": "France",
    "ES": "Spain",
    "PT": "Portugal",
    # "EE": "Estonia",
    # "DK": "Denmark",
    # "SE": "Sweden",
    # "NL": "Netherlands"
}

# %%
df_model.head()

# %%
df_model.head().to_dict(orient='records')
# %%
df_model.to_csv('./data/eurobarometer_preparedness_model_data_v2.csv', index=False)