app.py

import json    
import numpy as np    
import tensorflow as tf    
from tensorflow.keras import layers    
import sentencepiece as spm    
import requests  
import gradio as gr  
  
sp = spm.SentencePieceProcessor()  
sp.load("kolig_unigram.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 = 100  
  
def text_to_ids(text):  
    return sp.encode(text, out_type=int)  
  
def ids_to_text(ids):  
    return sp.decode(ids)  

class RotaryPositionalEmbedding(layers.Layer):  
    def __init__(self, dim):  
        super().__init__()  
        inv_freq = 1.0 / (10000 ** (np.arange(0, dim, 2) / dim))  
        self.inv_freq = tf.constant(inv_freq, dtype=tf.float32)  
  
    def call(self, x):  
        batch, heads, seq_len, depth = tf.unstack(tf.shape(x))  
        t = tf.range(seq_len, dtype=tf.float32)  
        freqs = tf.einsum('i,j->ij', t, self.inv_freq)  
        emb_sin = tf.sin(freqs)  
        emb_cos = tf.cos(freqs)  
        emb_cos = tf.reshape(emb_cos, [1, 1, seq_len, -1])  
        emb_sin = tf.reshape(emb_sin, [1, 1, seq_len, -1])  
        x1 = x[..., ::2]  
        x2 = x[..., 1::2]  
        x_rotated = tf.stack([  
            x1 * emb_cos - x2 * emb_sin,  
            x1 * emb_sin + x2 * emb_cos  
        ], axis=-1)  
        x_rotated = tf.reshape(x_rotated, tf.shape(x))  
        return x_rotated

class SwiGLU(tf.keras.layers.Layer):
    def __init__(self, d_model, d_ff):
        super().__init__()
        self.proj = tf.keras.layers.Dense(d_ff * 2)
        self.out = tf.keras.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.silu(x_gate))
        
class GPTBlock(tf.keras.layers.Layer):
    def __init__(self, d_model, d_ff, num_heads=8, dropout_rate=0.1, adapter_dim=64):  
        super().__init__()  
        self.ln1 = tf.keras.layers.LayerNormalization(epsilon=1e-5)  
        self.mha = tf.keras.layers.MultiHeadAttention(num_heads=num_heads, key_dim=d_model // num_heads)  
        self.dropout1 = tf.keras.layers.Dropout(dropout_rate) 
        self.adapter_down = tf.keras.layers.Dense(adapter_dim, activation='gelu') 
        self.adapter_up = tf.keras.layers.Dense(d_model)  
  
        self.ln2 = tf.keras.layers.LayerNormalization(epsilon=1e-5)  
        self.ffn = SwiGLU(d_model, d_ff)  
        self.dropout2 = tf.keras.layers.Dropout(dropout_rate) 
        self.rope = RotaryPositionalEmbedding(d_model // num_heads)  
  
    def call(self, x, training=False):  
        x_norm = self.ln1(x)  
        b, s, _ = tf.shape(x_norm)[0], tf.shape(x_norm)[1], tf.shape(x_norm)[2]  
        h = self.mha.num_heads  
        d = x_norm.shape[-1] // h  
  
        qkv = tf.reshape(x_norm, [b, s, h, d])  
        qkv = tf.transpose(qkv, [0, 2, 1, 3])  
        q = self.rope(qkv)  
        k = self.rope(qkv)  
        q = tf.reshape(tf.transpose(q, [0, 2, 1, 3]), [b, s, h * d])  
        k = tf.reshape(tf.transpose(k, [0, 2, 1, 3]), [b, s, h * d])  
  
        attn_out = self.mha(query=q, value=x_norm, key=k, use_causal_mask=True, training=training)  
        attn_out = self.dropout1(attn_out, training=training)  

        adapter_out = self.adapter_up(self.adapter_down(attn_out))
        attn_out = attn_out + adapter_out  
  
        x = x + attn_out  
        ffn_out = self.ffn(self.ln2(x))  
        x = x + self.dropout2(ffn_out, training=training)  
        return x

class InteractGPT(tf.keras.Model):  
    def __init__(self, vocab_size, seq_len, d_model, d_ff, n_layers, num_heads=8, dropout_rate=0.1):  
        super().__init__()  
        self.token_embedding = tf.keras.layers.Embedding(vocab_size, d_model)  
        self.blocks = [GPTBlock(d_model, d_ff, num_heads, dropout_rate) for _ in range(n_layers)]  
        self.ln_f = tf.keras.layers.LayerNormalization(epsilon=1e-5)  
  
    def call(self, x, training=False):  
        x = self.token_embedding(x)  
        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  

model = InteractGPT(vocab_size=vocab_size, seq_len=max_len, d_model=256, d_ff=1024, 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("InteractGPT.weights.h5")  
print("모델 가중치 로드 완료!")  

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

def generate_text_better_sampling(model, prompt, max_len=100, max_gen=98, top_k=50, p=0.9, temperature=0.8, min_len=20, repetition_penalty=1.2):
    model_input = text_to_ids(f"<start> {prompt} <sep>")
    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()

        # 반복 페널티 (frequency penalty)
        token_counts = {}
        for t in generated:
            token_counts[t] = token_counts.get(t, 0) + 1
        for token_id, count in token_counts.items():
            next_token_logits[token_id] /= (repetition_penalty ** count)

        # 최소 길이 넘으면 종료 토큰 확률 낮추기
        if len(generated) >= min_len:
            next_token_logits[end_id] -= 5.0
        next_token_logits[pad_id] -= 10.0

        logits_temp = next_token_logits / temperature
        probs = tf.nn.softmax(logits_temp).numpy()

        # top-k 필터링
        top_k_indices = np.argpartition(probs, -top_k)[-top_k:]
        top_k_probs = probs[top_k_indices]
        top_k_probs /= top_k_probs.sum()

        # top-p 필터링 (cumulative sum with side='left')
        sorted_indices = top_k_indices[np.argsort(top_k_probs)[::-1]]
        sorted_probs = np.sort(top_k_probs)[::-1]
        cumulative_probs = np.cumsum(sorted_probs)
        cutoff = np.searchsorted(cumulative_probs, p, side='left') + 1
        filtered_indices = sorted_indices[:cutoff]
        filtered_probs = sorted_probs[:cutoff]
        filtered_probs /= filtered_probs.sum()

        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(('.', '!', '?', '<end>')):
            break

        yield decoded_text


nickname = "사용자"

def respond(message, chat_history):
    message = message.replace("@사용자1@", nickname)
    response = ""
    for partial in generate_text_better_sampling(model, message):
        response = partial
        yield response

chat = gr.ChatInterface(
    fn=respond,
    title="InteractGPT",
)

chat.launch()