import json
from .feature_extractor import FeatureExtractor
from .recommender import Recommender
from .ranking_engine import RankingEngine
from .explanation_generator import ExplanationGenerator

class ExplainableRecsPipeline:
    def __init__(self):
        self.f=FeatureExtractor(); self.r=Recommender()
        self.k=RankingEngine(); self.e=ExplanationGenerator()

    def __call__(self,u_path,i_path):
        u=json.load(open(u_path))[0]
        items=json.load(open(i_path))
        uf=self.f.extract_user_features(u)
        scored=[]
        for it in items:
            itf=self.f.extract_item_features(it)
            s=self.r.score(uf,itf)
            exp=self.e.generate(uf,itf)
            scored.append({'item_id':it['id'],'score':s,'explanation':exp})
        return self.k.rank(scored)