import os
import json
from keras.models import load_model
from keras_preprocessing.sequence import pad_sequences
from keras_preprocessing.text import tokenizer_from_json

BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Risale alla root
MODEL_PATH = os.path.join(BASE_DIR, 'data', 'model', 'multi-classification.h5')
TOKENIZER_PATH = os.path.join(BASE_DIR, 'data', 'tokenizer', 'multi-classification-tokenizer.json')

CLASS_NAMES = ['Economia', 'Politica', 'Scienza_e_tecnica', 'Sport', 'Storia']

# Caricamento Singleton (lo carichiamo una volta sola)
model = None
tokenizer = None

def load_resources():
    global model, tokenizer
    if model is None and os.path.exists(MODEL_PATH):
        try:
            # Carica Tokenizer
            with open(TOKENIZER_PATH, 'r') as f:
                tokenizer_data = json.load(f)
                tokenizer = tokenizer_from_json(tokenizer_data)
            
            # Carica Modello
            model = load_model(MODEL_PATH, compile=False)
            return model, tokenizer
        except Exception as e:
            print(f"Errore caricamento risorse MultiLabel: {e}")
            return None, None

def multi_classification(text):
    global model, tokenizer
    if model is None or tokenizer is None:
        load_resources()
    if model is None or tokenizer is None:
        return {"Errore": "Modello non caricato"}
    try:
        # Preprocessing identico al training
        # 1. Tokenization
        sequences = tokenizer.texts_to_sequences([text])
        new_sequences = []
        for sequence in sequences[0]:
            if(sequence is None):
                sequence = 1
            new_sequences.append(sequence)
        x_new_sequences = [new_sequences]
        # 2. Padding (maxlen=200 come da tuo codice originale)
        data_padded = pad_sequences(x_new_sequences, maxlen=200)
        # 3. Predizione
        prediction = model.predict(data_padded, verbose=0)[0]
        # 4. Formattazione Output per Gradio {Label: Score}
        results = {}
        for i, score in enumerate(prediction):
            label = CLASS_NAMES[i].replace('_', ' ')
            results[label] = float(score)
        return results
    except Exception as e:
        return {f"Errore durante l'analisi: {str(e)}": 0.0}