import json
import pandas as pd
from sqlalchemy import create_engine
from sqlalchemy.types import Integer, String, Float
from dotenv import load_dotenv
import os

# Load environment variables from .env file
load_dotenv()

# Database connection
DB_USER = os.getenv('DB_USER')
DB_PASSWORD = os.getenv('DB_PASSWORD')
DB_HOST = os.getenv('DB_HOST')
DB_PORT = os.getenv('DB_PORT')
DB_NAME = os.getenv('DB_NAME')

# Create the connection string
db_connection_str = f'mysql+pymysql://{DB_USER}:{DB_PASSWORD}@{DB_HOST}:{DB_PORT}/{DB_NAME}'
engine = create_engine(db_connection_str)

# Load the JSON data as before
with open('CIK0001937441.json') as f:
    data = json.load(f)

cik = data.get('cik')
entity_name = data.get('entityName')
facts = data.get('facts')

rows = []

for taxonomy, fact_items in facts.items():
    for fact_id, fact_data in fact_items.items():
        label = fact_data.get('label')
        description = fact_data.get('description')
        units = fact_data.get('units')
        
        for unit, details_list in units.items():
            for details in details_list:
                row = {
                    'entity_cik': cik,
                    'entity_name': entity_name,
                    'fact_id': fact_id,
                    'fact_taxonomy': taxonomy,
                    'fact_label': label,
                    'fact_description': description,
                    'fact_unit': unit
                }
                row.update(details)
                rows.append(row)

# Create DataFrame from collected rows
df = pd.DataFrame(rows)

# Define DataFrame to SQL types (optional, but recommended for performance)
dtype_map = {
    'entity_cik': String(10),
    'entity_name': String(255),
    'fact_id': String(255),
    'fact_taxonomy': String(255),
    'fact_label': String(255),
    'fact_description': String(255),
    'fact_unit': String(50),
    'start': String(10),
    'end': String(10),
    'val': Float,
    'accn': String(50),
    'fy': Integer,
    'fp': String(10),
    'form': String(10),
    'filed': String(10),
    'frame': String(50)
}

# Write DataFrame to the 'data' table in the MariaDB database, create the table if it doesn't exist
df.to_sql('data', con=engine, if_exists='replace', index=False, dtype=dtype_map)

print("Data successfully written to the database.")