from airflow import DAG
from airflow.operators.python import PythonOperator
from airflow.utils.dates import days_ago
from datetime import timedelta
import pandas as pd
import logging
import sys
import pickle

sys.path.insert(0, '/opt/airflow')

from procesing.context import PipelineContext
from procesing.providers import SupabaseProvider, BackendAPIProvider
from procesing.steps import (
    FetchInteractionsStep,
    FetchPriceLogsStep,
    ComputeDemandStep,
    AggregatePriceLogsStep,
    JoinProductFeaturesStep,
)
from procesing.pricers.simple import SimpleSurgePricer

STORE_MODE = 'airline'

default_args = {
    'owner': 'phantom-research',
    'depends_on_past': False,
    'email_on_failure': False,
    'email_on_retry': False,
    'retries': 2,
    'retry_delay': timedelta(minutes=5),
}

def get_provider():
    class CompositeProvider(SupabaseProvider, BackendAPIProvider):
        def __init__(self):
            SupabaseProvider.__init__(self)
            BackendAPIProvider.__init__(self)
    return CompositeProvider()

def get_context(**kwargs):
    return PipelineContext(
        provider=get_provider(),
        store_mode=STORE_MODE,
    )

def fetch_interactions(**kwargs):
    context = get_context(**kwargs)
    step = FetchInteractionsStep(context)
    df = step.transform(None)
    kwargs['ti'].xcom_push(key='interactions_raw', value=pickle.dumps(df))
    logging.info(f"[{STORE_MODE}] Fetched {len(df)} interaction records")
    return len(df)

def fetch_price_logs(**kwargs):
    context = get_context(**kwargs)
    step = FetchPriceLogsStep(context)
    df = step.transform(None)
    kwargs['ti'].xcom_push(key='price_logs_raw', value=pickle.dumps(df))
    logging.info(f"[{STORE_MODE}] Fetched {len(df)} price records")
    return len(df)

def compute_demand(**kwargs):
    ti = kwargs['ti']
    df = pickle.loads(ti.xcom_pull(key='interactions_raw'))
    context = get_context(**kwargs)
    step = ComputeDemandStep(context)
    demand_df = step.transform(df)
    ti.xcom_push(key='demand_data', value=pickle.dumps(demand_df))
    logging.info(f"[{STORE_MODE}] Computed demand for {len(demand_df)} products")
    return len(demand_df)

def aggregate_price_logs(**kwargs):
    ti = kwargs['ti']
    df = pickle.loads(ti.xcom_pull(key='price_logs_raw'))
    context = get_context(**kwargs)
    step = AggregatePriceLogsStep(context)
    price_df = step.transform(df)
    ti.xcom_push(key='price_data', value=pickle.dumps(price_df))
    logging.info(f"[{STORE_MODE}] Aggregated price logs for {len(price_df)} products")
    return len(price_df)

def join_product_features(**kwargs):
    ti = kwargs['ti']
    demand_df = pickle.loads(ti.xcom_pull(key='demand_data'))
    price_df = pickle.loads(ti.xcom_pull(key='price_data'))
    context = get_context(**kwargs)
    step = JoinProductFeaturesStep(context)
    joined_df = step.transform((demand_df, price_df))
    ti.xcom_push(key='product_features', value=pickle.dumps(joined_df))
    logging.info(f"[{STORE_MODE}] Joined features for {len(joined_df)} products")
    return len(joined_df)

def apply_surge_pricing(**kwargs):
    ti = kwargs['ti']
    product_features = pickle.loads(ti.xcom_pull(key='product_features'))
    dag_conf = kwargs.get('dag_run').conf if kwargs.get('dag_run') else {}

    data = product_features.rename(columns={'demand_score': 'demand'})
    surge_pricer = SimpleSurgePricer(
        high_threshold=dag_conf.get('high_threshold', 10),
        low_threshold=dag_conf.get('low_threshold', 2),
        surge_multiplier=dag_conf.get('surge_multiplier', 1.2),
        discount_multiplier=dag_conf.get('discount_multiplier', 0.9)
    )
    surge_pricer.fit(data)
    data['optimal_price'] = surge_pricer.predict()

    prices_df = data[['productId', 'price', 'base_price', 'optimal_price', 'demand']].rename(columns={
        'price': 'current_price',
        'demand': 'demand_score'
    })
    ti.xcom_push(key='predicted_prices', value=pickle.dumps(prices_df))
    logging.info(f"[{STORE_MODE}] Applied surge pricing for {len(prices_df)} products")
    return len(prices_df)

def publish_results(**kwargs):
    ti = kwargs['ti']
    prices_df = pickle.loads(ti.xcom_pull(key='predicted_prices'))

    sys.path.insert(0, '/opt/airflow')
    from lib.model_registry import ModelRegistry

    registry = ModelRegistry()
    dag_conf = kwargs.get('dag_run').conf if kwargs.get('dag_run') else {}

    metadata = {
        'timestamp': pd.Timestamp.now().isoformat(),
        'store_mode': STORE_MODE,
        'dag_run_id': kwargs['dag_run'].run_id if kwargs.get('dag_run') else 'manual',
        'pricing_method': 'surge',
        'high_threshold': dag_conf.get('high_threshold', 10),
        'low_threshold': dag_conf.get('low_threshold', 2),
        'surge_multiplier': dag_conf.get('surge_multiplier', 1.2),
        'discount_multiplier': dag_conf.get('discount_multiplier', 0.9)
    }

    registry.publish_prices(prices_df, model_name=f'{STORE_MODE}_latest', metadata=metadata)
    logging.info(f"[{STORE_MODE}] Published surge pricing for {len(prices_df)} products")

    return {
        'n_products': len(prices_df),
        'registry_status': 'success',
        'store_mode': STORE_MODE,
        'mean_demand': float(prices_df['demand_score'].mean()) if 'demand_score' in prices_df.columns else None
    }


with DAG(
    f'surge_pricing_{STORE_MODE}',
    default_args=default_args,
    description=f'Surge pricing pipeline for {STORE_MODE} store mode',
    schedule_interval='*/15 * * * *',
    start_date=days_ago(1),
    catchup=False,
    max_active_runs=1,
    tags=['pricing', 'surge', 'research', STORE_MODE],
) as dag:

    t_fetch_interactions = PythonOperator(
        task_id='fetch_interactions',
        python_callable=fetch_interactions,
        provide_context=True,
    )

    t_fetch_price_logs = PythonOperator(
        task_id='fetch_price_logs',
        python_callable=fetch_price_logs,
        provide_context=True,
    )

    t_compute_demand = PythonOperator(
        task_id='compute_demand',
        python_callable=compute_demand,
        provide_context=True,
    )

    t_aggregate_prices = PythonOperator(
        task_id='aggregate_price_logs',
        python_callable=aggregate_price_logs,
        provide_context=True,
    )

    t_join_features = PythonOperator(
        task_id='join_product_features',
        python_callable=join_product_features,
        provide_context=True,
    )

    t_surge_pricing = PythonOperator(
        task_id='apply_surge_pricing',
        python_callable=apply_surge_pricing,
        provide_context=True,
    )

    t_publish = PythonOperator(
        task_id='publish_results',
        python_callable=publish_results,
        provide_context=True,
    )

    t_fetch_interactions >> t_compute_demand
    t_fetch_price_logs >> t_aggregate_prices
    [t_compute_demand, t_aggregate_prices] >> t_join_features >> t_surge_pricing >> t_publish