app.py

import json      
import numpy as np      
import tensorflow as tf      
from tensorflow.keras import layers      
import gradio as gr      
import re    
import requests    
import math    
import sentencepiece as spm

# SentencePiece 로드 (토크나이저랑 특수 토큰 ID도 동일하게 세팅)
sp = spm.SentencePieceProcessor()
sp.load("ko_unigram3.model")

pad_id = sp.piece_to_id("<pad>")
if pad_id == -1: pad_id = 0
start_id = sp.piece_to_id("<start>")
if start_id == -1: start_id = 1
end_id = sp.piece_to_id("< end >")
if end_id == -1: end_id = 2
unk_id = sp.piece_to_id("<unk>")
if unk_id == -1: unk_id = 3

vocab_size = sp.get_piece_size()
max_len = 128

def text_to_ids(text):
    return sp.encode(text, out_type=int)

def ids_to_text(ids):
    return sp.decode(ids)

# GEGLU 레이어
class GEGLU(tf.keras.layers.Layer):
    def __init__(self, d_model, d_ff):
        super().__init__()
        self.proj = layers.Dense(d_ff * 2)
        self.out = layers.Dense(d_model)
    def call(self, x):
        x_proj = self.proj(x)
        x_val, x_gate = tf.split(x_proj, 2, axis=-1)
        return self.out(x_val * tf.nn.gelu(x_gate))

# GPT 블록
class GPTBlock(tf.keras.layers.Layer):
    def __init__(self, d_model, d_ff, num_heads=16, dropout_rate=0.1):
        super().__init__()
        self.ln1 = layers.LayerNormalization(epsilon=1e-5)
        self.attn = layers.MultiHeadAttention(num_heads=num_heads, key_dim=d_model // num_heads)
        self.dropout1 = layers.Dropout(dropout_rate)
        self.ln2 = layers.LayerNormalization(epsilon=1e-5)
        self.ffn = GEGLU(d_model, d_ff)
        self.dropout2 = layers.Dropout(dropout_rate)
    def call(self, x, training=False):
        x_norm = self.ln1(x)
        attn_out = self.attn(query=x_norm, value=x_norm, key=x_norm,
                             use_causal_mask=True, training=training)
        x = x + self.dropout1(attn_out, training=training)
        ffn_out = self.ffn(self.ln2(x))
        x = x + self.dropout2(ffn_out, training=training)
        return x

# GPT 모델
class GPT(tf.keras.Model):
    def __init__(self, vocab_size, seq_len, d_model, d_ff, n_layers, num_heads=16, dropout_rate=0.1):
        super().__init__()
        self.token_embedding = layers.Embedding(vocab_size, d_model)
        self.pos_embedding = self.add_weight(
            name="pos_embedding",
            shape=[seq_len, d_model],
            initializer=tf.keras.initializers.RandomNormal(stddev=0.01)
        )
        self.blocks = [GPTBlock(d_model, d_ff, num_heads, dropout_rate) for _ in range(n_layers)]
        self.ln_f = layers.LayerNormalization(epsilon=1e-5)
    def call(self, x, training=False):
        seq_len = tf.shape(x)[1]
        x = self.token_embedding(x) + self.pos_embedding[tf.newaxis, :seq_len, :]
        for block in self.blocks:
            x = block(x, training=training)
        x = self.ln_f(x)
        logits = tf.matmul(x, self.token_embedding.embeddings, transpose_b=True)
        return logits

# 모델 생성 & 가중치 불러오기
model = GPT(vocab_size=vocab_size, seq_len=max_len, d_model=128, d_ff=512, n_layers=6)
dummy_input = tf.zeros((1, max_len), dtype=tf.int32)  # 배치1, 시퀀스길이 max_len
_ = model(dummy_input)  # 모델이 빌드됨
model.load_weights("KeraLux3.weights.h5")
print("모델 가중치 로드 완료!")

def decode_sp_tokens(tokens):
    text = ''.join(tokens).replace('▁', ' ').strip()
    return text

def generate_text_topkp_stream(model, prompt, max_len=100, max_gen=98, p=0.9, k=50, temperature=0.8, min_len=20):
    model_input = text_to_ids(f"<start> {prompt}")
    model_input = model_input[:max_len]
    generated = list(model_input)
    text_so_far = []

    for step in range(max_gen):
        pad_length = max(0, max_len - len(generated))
        input_padded = np.pad(generated, (0, pad_length), constant_values=pad_id)
        input_tensor = tf.convert_to_tensor([input_padded])
        logits = model(input_tensor, training=False)
        next_token_logits = logits[0, len(generated) - 1].numpy()

        if len(generated) >= min_len:
            next_token_logits[end_id] -= 5.0
        next_token_logits[pad_id] -= 10.0

        # 온도 적용
        logits_temp = next_token_logits / temperature

        # 1. 확률 계산
        probs = tf.nn.softmax(logits_temp).numpy()

        # 2. Top-k 필터링
        top_k_indices = np.argpartition(probs, -k)[-k:]
        top_k_probs = probs[top_k_indices]

        # 3. Top-p 필터링 (누적합 계산용 정렬)
        sorted_idx = np.argsort(top_k_probs)[::-1]
        top_k_indices = top_k_indices[sorted_idx]
        top_k_probs = top_k_probs[sorted_idx]
        cumulative_probs = np.cumsum(top_k_probs)

        # p 넘는 부분 자르기
        cutoff = np.searchsorted(cumulative_probs, p, side='right') + 1

        filtered_indices = top_k_indices[:cutoff]
        filtered_probs = top_k_probs[:cutoff]

        # 확률 정규화
        filtered_probs /= filtered_probs.sum()

        # 샘플링
        next_token_id = np.random.choice(filtered_indices, p=filtered_probs)

        generated.append(int(next_token_id))
        next_word = sp.id_to_piece(int(next_token_id))
        text_so_far.append(next_word)

        decoded_text = decode_sp_tokens(text_so_far)

        if len(generated) >= min_len and next_token_id == end_id:
            break
        if len(generated) >= min_len and decoded_text.endswith(('.', '!', '?')):
            break

        yield decoded_text

def chat(user_input, history):      
    if history is None:      
        history = []      

    for partial_response in generate_text_topkp_stream(model, user_input, p=0.9):      
        yield history + [(user_input, partial_response)], history + [(user_input, partial_response)]

with gr.Blocks(title="KeraLux Chat") as demo:      
    gr.Markdown(      
        """      
        # 💡 KeraLux와 대화해보세요!      
        대화를 입력하면 KeraLux가 똑똑하게 대답해줄 거예요.      
        """,      
        elem_id="title",      
    )      
    gr.Markdown("---")      
      
    with gr.Row():      
        with gr.Column(scale=1):      
            chatbot = gr.Chatbot(label="KeraLux 채팅창", bubble_full_width=False)      
        with gr.Column(scale=0):      
            msg = gr.Textbox(      
                label="당신의 질문을 입력하세요!",      
                placeholder="ex) 나 좀 도와줄 수 있니?",      
                lines=1,      
            )      
            state = gr.State([])      
      
    msg.submit(chat, inputs=[msg, state], outputs=[chatbot, state])      
    msg.submit(lambda: "", None, msg)  # 입력창 초기화      
      
demo.launch(share=True)