mirror of
https://github.com/velocitatem/PHANTOM.git
synced 2026-05-31 16:43:36 +00:00
ef98141ca8
* chore: update provider and pricing snitch with agnostic system * cloning pipelines per mode instance * updating airline hero section * fix: must keep airflow secretkey * fix: fixture update to hotel not shop * chore: refactored to factory design pattern of pipelines * chore: clean up definition of composite class of providers
43 lines
1.7 KiB
Python
Executable File
43 lines
1.7 KiB
Python
Executable File
import os
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import pandas as pd
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import requests
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from typing import List
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from supabase import create_client, Client
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from procesing.providers.base import DataProvider
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from dotenv import load_dotenv
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class SupabaseProvider(DataProvider):
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"""Concrete Supabase + backend API implementation"""
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def __init__(self,
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supabase_url: str = None,
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supabase_key: str = None,):
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load_dotenv()
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self.supabase_url = supabase_url or os.getenv("NEXT_PUBLIC_SUPABASE_URL")
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self.supabase_key = supabase_key or os.getenv("NEXT_PUBLIC_SUPABASE_ANON_KEY")
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self.supabase: Client = create_client(self.supabase_url, self.supabase_key)
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def fetch_products(self, store_mode: str) -> pd.DataFrame:
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# hotel uses room_type, airline uses flight_type; select all and normalize
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resp = self.supabase.table(f'{store_mode}_products').select("*").execute()
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if not resp.data:
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return pd.DataFrame()
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df = pd.DataFrame(resp.data)
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# normalize type column: hotel has room_type, airline has flight_type
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if 'room_type' in df.columns:
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df['product_type'] = df['room_type']
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elif 'flight_type' in df.columns:
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df['product_type'] = df['flight_type']
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return df
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def fetch_experiments(self, experiment_ids: List[str]) -> pd.DataFrame:
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if not experiment_ids:
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return pd.DataFrame()
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resp = self.supabase.table('experiments').select(
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'id, subject_name, xp_human_only, xp_market_mode, xp_task_id, '
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'task:tasks(task_name, task_description, task_def_of_done)'
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).in_('id', experiment_ids).execute()
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return pd.DataFrame(resp.data) if resp.data else pd.DataFrame()
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