import numpy as np
import joblib
import torch
from transformers import AutoModel
import os

class FinancialFilingClassifier:
    def __init__(self, model_dir):
        self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
        print(f"Loading Jina Encoder on {self.device}...")
        self.encoder = AutoModel.from_pretrained(
            "jinaai/jina-embeddings-v3", 
            trust_remote_code=True, 
            torch_dtype=torch.float16 if self.device == 'cuda' else torch.float32
        ).to(self.device)
        
        print("Loading XGBoost Cascade...")
        self.router = joblib.load(os.path.join(model_dir, "router_xgb.pkl"))
        self.router_le = joblib.load(os.path.join(model_dir, "router_le.pkl"))
        self.specialists = {}
        self.model_dir = model_dir

    def _get_vector(self, text):
        log_len = np.log1p(len(str(text)))
        with torch.no_grad():
            vec = self.encoder.encode([text], task="classification", max_length=8192)
        return np.hstack([vec, [[log_len]]])

    def _load_specialist(self, category):
        safe_name = category.replace(" ", "_").replace("&", "and").replace("/", "_")
        if safe_name not in self.specialists:
            try:
                clf = joblib.load(os.path.join(self.model_dir, f"specialist_{safe_name}_xgb.pkl"))
                le = joblib.load(os.path.join(self.model_dir, f"specialist_{safe_name}_le.pkl"))
                self.specialists[safe_name] = (clf, le)
            except FileNotFoundError:
                return None
        return self.specialists[safe_name]

    def predict(self, text):
        vector = self._get_vector(text)
        router_probs = self.router.predict_proba(vector)[0]
        top_indices = np.argsort(router_probs)[::-1][:2]
        
        candidates = []
        for idx in top_indices:
            category = self.router_le.classes_[idx]
            router_conf = router_probs[idx]
            specialist = self._load_specialist(category)
            
            if specialist:
                clf, le = specialist
                spec_probs = clf.predict_proba(vector)[0]
                best_idx = np.argmax(spec_probs)
                label = le.classes_[best_idx]
                spec_conf = spec_probs[best_idx]
                combined_score = np.sqrt(router_conf * spec_conf)
                candidates.append({"category": category, "label": label, "score": float(combined_score)})
            else:
                candidates.append({"category": category, "label": category, "score": float(router_conf)})
        
        return max(candidates, key=lambda x: x['score'])