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3a24a7b 2ff9250 96bc481 2ff9250 3a24a7b 2ff9250 3a24a7b 2ff9250 3a24a7b 96bc481 3a24a7b 96bc481 3a24a7b c469f55 96bc481 3a24a7b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 | 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} |