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
import io

# add parent dir to path so procesing package can be imported
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,
)

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():
    """Factory to create composite provider"""
    class CompositeProvider(SupabaseProvider, BackendAPIProvider): # TODO: Fix this into one global provider singelton instead of multiple inheritance declarations acoss the codebase
        def __init__(self):
            SupabaseProvider.__init__(self)
            BackendAPIProvider.__init__(self)
    return CompositeProvider()

def get_context(**kwargs):
    """Build pipeline context from Airflow config"""
    dag_conf = kwargs.get('dag_run').conf if kwargs.get('dag_run') else {}
    return PipelineContext(
        provider=get_provider(),
        store_mode=dag_conf.get('store_mode', 'hotel'),
    )

# atomic task functions (each wraps one sklearn step)
def fetch_interactions(**kwargs):
    """Task: Fetch interaction data from Kafka"""
    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"Fetched {len(df)} interaction records")
    return len(df)

def fetch_price_logs(**kwargs):
    """Task: Fetch price logs from Kafka"""
    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"Fetched {len(df)} price records")
    return len(df)

def compute_demand(**kwargs):
    """Task: Compute demand scores from interactions"""
    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"Computed demand for {len(demand_df)} products")
    return len(demand_df)

def aggregate_price_logs(**kwargs):
    """Task: Aggregate price logs"""
    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"Aggregated price logs for {len(price_df)} products")
    return len(price_df)

def join_product_features(**kwargs):
    """Task: Join demand and price data"""
    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"Joined features for {len(joined_df)} products")
    return len(joined_df)

def apply_surge_pricing(**kwargs):
    """Task: Apply surge pricing rules to generate optimal prices"""
    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 {}
    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)

    context = get_context(**kwargs)
    products = context.products
    results = []

    for pid in product_features['productId'].unique():
        prod_data = product_features[product_features['productId'] == pid]
        if prod_data.empty:
            continue

        demand = prod_data["demand_score"].mean()
        current_price = prod_data["price"].mean()

        prod_meta = products[products['id'] == pid]
        if not prod_meta.empty:
            meta = prod_meta.iloc[0]['metadata']
            base_price = meta.get('base_price', current_price) if isinstance(meta, dict) else current_price
        else:
            base_price = current_price

        if demand >= high_threshold:
            optimal_price = base_price * surge_multiplier
        elif demand <= low_threshold:
            optimal_price = base_price * discount_multiplier
        else:
            optimal_price = base_price

        results.append({
            'productId': pid,
            'current_price': current_price,
            'base_price': base_price,
            'optimal_price': optimal_price,
            'demand_score': demand
        })

    prices_df = pd.DataFrame(results)
    ti.xcom_push(key='predicted_prices', value=pickle.dumps(prices_df))
    logging.info(f"Applied surge pricing for {len(prices_df)} products")
    return len(prices_df)

def publish_results(**kwargs):
    """Task: Publish surge pricing results to registry"""
    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': dag_conf.get('store_mode', 'hotel'),
        '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='latest', metadata=metadata)

    logging.info(f"Published surge pricing for {len(prices_df)} products")

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


# DAG definition
with DAG(
    'surge_pricing_pipeline',
    default_args=default_args,
    description='Simple surge pricing pipeline: demand aggregation + rule-based pricing',
    schedule_interval='*/15 * * * *',
    start_date=days_ago(1),
    catchup=False,
    max_active_runs=1,
    tags=['pricing', 'surge', 'research', 'simplified'],
) as dag:

    # parallel data fetching
    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,
    )

    # compute demand from interactions
    t_compute_demand = PythonOperator(
        task_id='compute_demand',
        python_callable=compute_demand,
        provide_context=True,
    )

    # aggregate price logs
    t_aggregate_prices = PythonOperator(
        task_id='aggregate_price_logs',
        python_callable=aggregate_price_logs,
        provide_context=True,
    )

    # join demand and prices
    t_join_features = PythonOperator(
        task_id='join_product_features',
        python_callable=join_product_features,
        provide_context=True,
    )

    # apply surge pricing
    t_surge_pricing = PythonOperator(
        task_id='apply_surge_pricing',
        python_callable=apply_surge_pricing,
        provide_context=True,
    )

    # publish to registry
    t_publish = PythonOperator(
        task_id='publish_results',
        python_callable=publish_results,
        provide_context=True,
    )

    # dependency graph: parallel fetch -> process -> join -> surge -> publish
    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