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Airflow addition (#28)
* 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)
This commit is contained in:
Daniel Alves Rösel
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GitHub
GitHub
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182
backend/provider/app.py
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182
backend/provider/app.py
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from fastapi import FastAPI, HTTPException, Query
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from fastapi.middleware.cors import CORSMiddleware
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from pydantic import BaseModel
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from typing import Literal, Optional
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import uvicorn, os, sys
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from supabase import create_client, Client
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from dotenv import load_dotenv
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import numpy as np
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import pandas as pd
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load_dotenv()
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# Local imports of registry and pricing function
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sys.path.append(os.path.dirname(os.path.abspath(__file__))+ "/../../experiments/")
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from procesing.providers import SupabaseProvider, BackendAPIProvider
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from procesing.pricers import (
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StaticPricer,
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RandomPricer,
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ElasticityBasedPricer
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)
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from procesing.steps import (
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StateSpace,
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PredictPricesStep
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)
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from procesing import PipelineContext
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sys.path.append(os.path.dirname(os.path.abspath(__file__))+ "/../../lib/")
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from lib.model_registry import ModelRegistry
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# Config
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app = FastAPI(title="PHANTOM Pricing Provider")
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app.add_middleware(CORSMiddleware, allow_origins=["*"], allow_credentials=True, allow_methods=["*"], allow_headers=["*"])
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supabase: Client = create_client(os.getenv("NEXT_PUBLIC_SUPABASE_URL"), os.getenv("NEXT_PUBLIC_SUPABASE_ANON_KEY"))
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registry = ModelRegistry()
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class PriceResponse(BaseModel):
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productId: str
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price: float
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base_price: float
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markup: float
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elasticity: Optional[float] = None
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model_version: str = 'latest'
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@app.get("/health")
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def health() -> dict:
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return {"status": "healthy", "redis": registry.health_check()}
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@app.get("/api/{mode}/price/{productId}", response_model=PriceResponse)
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def get_price(mode: Literal['hotel', 'airline'], productId: str, sessionId: Optional[str] = Query(None), experimentId: Optional[str] = Query(None)):
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product = supabase.table(f'{mode}_products').select("metadata").eq('id', productId).execute().data[0]
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if not product: raise HTTPException(404, f"Product {productId} not found")
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metadata = product['metadata']
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base_price = metadata.get('base_price', 100.0)
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class Provider(SupabaseProvider, BackendAPIProvider):
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def __init__(self, backend_url: str):
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SupabaseProvider.__init__(self)
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BackendAPIProvider.__init__(self, backend_url=backend_url)
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context = PipelineContext(
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provider=Provider(backend_url=os.getenv("BACKEND_URL")),
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store_mode=mode
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)
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pricing_model = registry.get_pricing_model('latest')
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elasticity_df = registry.get_elasticity('latest')
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if pricing_model is None or elasticity_df is None:
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return PriceResponse(
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productId=productId,
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price=base_price,
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base_price=base_price,
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markup=1.0,
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elasticity=None
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)
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products = context.products
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if products.empty:
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raise HTTPException(500, "No products available in catalog")
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# merge elasticity with product base prices
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products_with_meta = products.copy()
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products_with_meta['base_price'] = products_with_meta['metadata'].apply(
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lambda m: m.get('base_price', 100.0) if isinstance(m, dict) else 100.0
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)
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merged = products_with_meta[['id', 'base_price']].rename(
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columns={'id': 'productId'}
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).merge(
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elasticity_df[['productId', 'elasticity']],
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on='productId',
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how='left'
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).fillna({'elasticity': 0.0})
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# compute demand: use pricer's mean_demand if available, else default
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demand_values = (pricing_model.mean_demand
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if hasattr(pricing_model, 'mean_demand') and pricing_model.mean_demand is not None
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else np.ones(len(merged)) * 10.0)
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# build state space with session features if sessionId provided
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session_features = pd.DataFrame()
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if sessionId:
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try:
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# fetch recent session interactions from backend
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from procesing.steps.session import ExtractSessionFeaturesStep
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import requests
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from datetime import datetime, timedelta
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t_end = datetime.utcnow()
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t_start = t_end - timedelta(hours=1)
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backend_url = os.getenv("BACKEND_URL")
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print(backend_url)
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resp = requests.get(
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f"{os.getenv('BACKEND_URL')}/api/kafka/dump", # TODO: THIS IS SHIT, must fix this
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params={'topic': 'user-interactions', 't_start': t_start.isoformat(), 't_end': t_end.isoformat()},
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timeout=2
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)
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if resp.ok:
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msgs = resp.json().get('messages', [])
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interactions_df = pd.DataFrame(msgs)
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if not interactions_df.empty and 'sessionId' in interactions_df.columns:
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session_interactions = interactions_df[interactions_df['sessionId'] == sessionId]
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if not session_interactions.empty:
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extractor = ExtractSessionFeaturesStep(context=context)
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session_features_df = extractor.transform(session_interactions)
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if not session_features_df.empty:
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session_features = session_features_df.drop(columns=['sessionId'])
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except Exception as e:
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print(f"[session-features-error] {e}")
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# continue without session features
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state = StateSpace(
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demand=demand_values,
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prices=merged['base_price'].values,
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session_features=session_features,
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product_ids=merged['productId'].values,
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elasticity=merged['elasticity'].values,
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metadata={'sessionId': sessionId, 'experimentId': experimentId}
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)
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oracle = PredictPricesStep(context=context)
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prices_df = oracle.transform((pricing_model, state))
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product_price_row = prices_df[prices_df['productId'] == productId]
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if product_price_row.empty:
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raise HTTPException(404, f"No pricing available for product {productId}")
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optimal_price = float(product_price_row['predicted_price'].iloc[0])
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product_elasticity_row = elasticity_df[elasticity_df['productId'] == productId]
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product_elasticity = (float(product_elasticity_row['elasticity'].iloc[0])
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if not product_elasticity_row.empty else None)
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return PriceResponse(
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productId=productId,
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price=optimal_price,
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base_price=base_price,
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markup=optimal_price/base_price,
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elasticity=product_elasticity
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)
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@app.get("/models")
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def list_models(): return registry.list_models()
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@app.post("/models/reload")
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def reload_models():
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elasticity, pricing_model = registry.get_elasticity('latest'), registry.get_pricing_model('latest')
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return {
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"elasticity_loaded": bool(elasticity),
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"n_products": len(elasticity) if elasticity is not None else 0,
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"pricing_model_loaded": bool(pricing_model),
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"model_class": pricing_model.__class__.__name__ if pricing_model else None
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}
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if __name__ == "__main__":
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uvicorn.run(app, host="0.0.0.0", port=int(os.getenv("PROVIDER_PORT", "5001")))
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