mirror of
https://github.com/velocitatem/PHANTOM.git
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ad9423bf59
* introducing airflow to run pipeline * chore: updating dag with upload to registry * introducing complete provider (non refactored and noisy) * chore: removing old shit * generic pricing baselines * feature: super simple model registry (to be updated maybe third party OS software) * chore: refactoring the providers docker config and requirements * chore: refactored and broke down components (braking * exporting all * local pipeline excution working * fix: fixing import structures from nonrelativistic * chore: enables cross comm pickling with fully e2e pipeline compilation * docs: what the pipeline is like now * pipelines local running and pipeline high level definition * cleaning old pipeline and vectorization * leaked but fixing, not so important * test: started with pipeline step testing * chore: cleaning up provider of prices * test: extra tests wit hsemantic meaning checks * migrating pricers * feature: introducing pricing predictors (pricers) * chore: e2e is done with new pipeline * extra session feature extraction * feature: experiemntal sessin pricer and metrics(vibe) * chore: redefined and connected pricers (#29)
36 lines
1.4 KiB
Python
Executable File
36 lines
1.4 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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resp = self.supabase.table(f'{store_mode}_products').select(
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"id, room_type, date_index, metadata, availability"
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).execute()
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return pd.DataFrame(resp.data) if resp.data else pd.DataFrame()
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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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