api.py

import requests
import numpy as np
import tensorflow as tf
import asyncio
from fastapi import FastAPI, Request
from fastapi.responses import StreamingResponse
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
import nltk
nltk.download('punkt')
from nltk.tokenize import 
    
app = FastAPI()    
      
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 generate_text_mirostat_top_p(model, prompt, max_len=100, max_gen=98,      
                                 temperature=1.0, min_len=20,      
                                 repetition_penalty=1.2, eta=0.1, m=100, p=0.9):      
    model_input = text_to_ids(f"<start> {prompt} <sep>")      
    model_input = model_input[:max_len]      
    generated = list(model_input)      
      
    tau = 5.0  # 초기 목표 surprise      
      
    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 적용      
        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      
      
        # 온도 조절      
        next_token_logits = next_token_logits / temperature      
      
        # --- 미로스타트 + Top-p 샘플링 ---      
        logits_stable = next_token_logits - np.max(next_token_logits)      
        probs = np.exp(logits_stable)      
        probs /= probs.sum()      
      
        # 1. mirostat top-m 후보 추리기      
        sorted_indices = np.argsort(-probs)      
        top_indices = sorted_indices[:m]      
        top_probs = probs[top_indices]      
        top_probs /= top_probs.sum()      
      
        # 2. mirostat 샘플링      
        sampled_index = np.random.choice(top_indices, p=top_probs)      
        sampled_prob = probs[sampled_index]      
        observed_surprise = -np.log(sampled_prob + 1e-9)      
        tau += eta * (observed_surprise - tau)      
      
        # 3. top-p 필터링      
        sorted_top_indices = top_indices[np.argsort(-top_probs)]      
        sorted_top_probs = np.sort(top_probs)[::-1]      
        cumulative_probs = np.cumsum(sorted_top_probs)      
        cutoff = np.searchsorted(cumulative_probs, p, side='left') + 1      
        filtered_indices = sorted_top_indices[:cutoff]      
        filtered_probs = sorted_top_probs[:cutoff]      
        filtered_probs /= filtered_probs.sum()      
      
        # 4. 최종 토큰 샘플링      
        final_token = np.random.choice(filtered_indices, p=filtered_probs)      
        generated.append(int(final_token))      
      
        decoded_text = sp.decode(generated)      
        # 특수 토큰 제거      
        for token in ["<start>", "<sep>", "<end>"]:      
            decoded_text = decoded_text.replace(token, "")      
      
        decoded_text = decoded_text.strip()      
      
        if len(generated) >= min_len and (final_token == end_id or decoded_text.endswith(('.', '!', '?'))):      
            yield decoded_text      
            break      
              
async def async_generator_wrapper(prompt: str):      
    # 동기 제너레이터를 비동기로 감싸기      
    loop = asyncio.get_event_loop()      
    gen = generate_text_mirostat_top_p(model, prompt)      
          
    for text_piece in gen:      
        yield text_piece      
        # 토큰 생성 속도 조절 (0.1초 딜레이)      
        await asyncio.sleep(0.1)      
      
@app.get("/generate")      
async def generate(request: Request):      
    # 쿼리 파라미터로 prompt 받음, 없으면 기본값      
    prompt = request.query_params.get("prompt", "안녕하세요")      
          
    # 스트리밍 응답으로 보냄      
    return StreamingResponse(async_generator_wrapper(prompt), media_type="text/plain") 여기에 합쳐

async def async_generator_wrapper(prompt: str):
    intent = simple_intent_classifier(prompt)

    if intent == "정보질문":
        wiki_summary = get_wikipedia_summary(prompt)
        summarized = summarize_text(wiki_summary, top_n=3)
        yield f"『 \"{prompt}\" 에 대한 위키백과 요약입니다. 』\n\n{summarized}\n\n"

    # 이후 일반 생성으로 이어감 (스트리밍)
    gen = generate_text_mirostat_top_p(model, prompt)
    for text_piece in gen:
        yield text_piece
        await asyncio.sleep(0.1)

@app.get("/generate")
async def generate(request: Request):
    prompt = request.query_params.get("prompt", "안녕하세요")
    return StreamingResponse(async_generator_wrapper(prompt), media_type="text/plain")