File size: 3,068 Bytes
e6434d7 | 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 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 | # serve_gru.py ────────────────────────────────────────────────
import re, numpy as np, tensorflow as tf
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing.text import tokenizer_from_json
# from huggingface_hub import hf_hub_download
# --- descarga desde tu Space/repo de HF ---
MODEL_PATH, TOK_PATH = "chatbot_seq2seq.keras", "tokenizer.json"
MAXLEN = 22
START, END = "<start>", "<end>"
# ── utilidades ------------------------------------------------
def _norm(s: str) -> str:
s = re.sub(r"[^a-zA-Z0-9?!.]+", " ", s.lower())
s = re.sub(r"([?.!])", r" \1 ", s)
return re.sub(r"\s+", " ", s).strip()
def _pad(seq):
return tf.keras.preprocessing.sequence.pad_sequences(
seq, maxlen=MAXLEN, padding="post"
)
# ── carga modelo y tokenizer ----------------------------------
print("‣ cargando modelo y tokenizer…", end="", flush=True)
model = load_model(MODEL_PATH)
with open(TOK_PATH, encoding="utf-8") as f:
tok = tokenizer_from_json(f.read())
emb_layer = model.get_layer("emb")
enc_gru = model.get_layer("enc_gru")
dec_gru = model.get_layer("dec_gru")
dense = model.get_layer("dense")
enc_model = tf.keras.Model(model.input[0], enc_gru.output[1])
dec_cell = dec_gru.cell
UNK_ID = tok.word_index["<unk>"]
START_ID = tok.word_index[START]
END_ID = tok.word_index[END]
print(" listo 🟢")
# ── paso único del decoder ------------------------------------
def _step(tok_id, state):
# token → embedding
x = tf.constant([[tok_id]], dtype=tf.int32) # (1,1)
x = emb_layer(x) # (1,1,emb)
x = tf.squeeze(x, axis=1) # (1,emb)
h, _ = dec_cell(x, states=state) # (1,units)
logits = dense(h)[0].numpy() # (vocab,)
logits[UNK_ID] = -1e9 # nunca <unk>
return logits, [h]
# ── función de inferencia greedy -----------------------------
def reply(msg: str, max_len: int = MAXLEN) -> str:
# normaliza y codifica
seq = _pad(tok.texts_to_sequences([f"{START} {_norm(msg)} {END}"]))
h_enc = enc_model.predict(seq, verbose=0) # (1,units)
state = [tf.convert_to_tensor(h_enc)] # [(1,units)]
tok_id, out_ids = START_ID, []
for _ in range(max_len):
logits, state = _step(tok_id, state)
# greedy: la más probable
tok_id = int(np.argmax(logits))
# condiciones de parada
if tok_id in (END_ID, START_ID):
break
if len(out_ids) >= 2 and tok_id == out_ids[-1] == out_ids[-2]:
break
out_ids.append(tok_id)
# reconstruye texto
return " ".join(tok.index_word[i] for i in out_ids) or "(sin respuesta)"
# ── demo CLI (opcional) ---------------------------------------
if __name__ == "__main__":
while True:
q = input("Tú: ").strip()
if not q: continue
print("Bot:", reply(q))
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