uploading streamlit logic

requirements.txt

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+streamlit==1.33.0
+tensorflow==2.15.0
+numpy>=1.20.0
+requests>=2.0
+huggingface-hub==0.32.0

serve_gru.py

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+# 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 = hf_hub_download(
+    repo_id="robertkm23/chat_bot", filename="chatbot_seq2seq.keras",
+    repo_type="model"
+)
+TOK_PATH = hf_hub_download(
+    repo_id="robertkm23/chat_bot", filename="tokenizer.json",
+    repo_type="model"
+)
+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))

streamlit_app.py

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+import streamlit as st
+from serve_gru import reply
+
+st.set_page_config(page_title="Chatbot GRU", page_icon="🤖")
+st.title("💬 Chatbot GRU (Cornell Movie Dialogs)")
+
+# Inicializa historial
+if "history" not in st.session_state:
+    st.session_state.history = []
+
+# Campo de chat integrado
+msg = st.chat_input("Escribe tu mensaje...")
+if msg:
+    # Añade mensaje del usuario
+    st.session_state.history.append(("user", msg))
+    # Obtiene respuesta del modelo
+    bot_resp = reply(msg)
+    st.session_state.history.append(("assistant", bot_resp))
+
+# Renderiza el chat
+for role, text in st.session_state.history:
+    st.chat_message(role).markdown(text)