import numpy as np
import tensorflow as tf
from tensorflow.keras import layers
import sentencepiece as spm
import gradio as gr
import requests
import os

# ----------------------
# 파일 다운로드 유틸
# ----------------------
def download_file(url, save_path):
    r = requests.get(url, stream=True)
    r.raise_for_status()
    with open(save_path, "wb") as f:
        for chunk in r.iter_content(8192*2):
            f.write(chunk)
    print(f"✅ {save_path} 저장됨")

MODEL_PATH = "encoder.weights.h5"
TOKENIZER_PATH = "bpe.model"

if not os.path.exists(MODEL_PATH):
    download_file(
        "https://huggingface.co/OpenLab-NLP/openlem1/resolve/main/encoder.weights.h5?download=true",
        MODEL_PATH
    )

if not os.path.exists(TOKENIZER_PATH):
    download_file(
        "https://huggingface.co/OpenLab-NLP/openlem1/resolve/main/bpe.model?download=true",
        TOKENIZER_PATH
    )

MAX_LEN = 128
EMBED_DIM = 384
LATENT_DIM = 384
DROPOUT_RATE = 0.01

# ===============================
# 1️⃣ 토크나이저 로딩
# ===============================
sp = spm.SentencePieceProcessor(TOKENIZER_PATH)
pad_id = sp.piece_to_id("<pad>") if sp.piece_to_id("<pad>") != -1 else 0
vocab_size = sp.get_piece_size()

def encode_sentence(sentence, max_len=MAX_LEN):
    return sp.encode(sentence, out_type=int)[:max_len]

def pad_sentence(tokens):
    return tokens + [pad_id]*(MAX_LEN - len(tokens))

class EncoderBlock(layers.Layer):
    def __init__(self, embed_dim=EMBED_DIM, latent_dim=LATENT_DIM):
        super().__init__()  # ✅ 반드시 맨 위에 추가
        self.mha = layers.MultiHeadAttention(num_heads=8, key_dim=embed_dim//8)
        self.WB = layers.Dense(1152)
        self.W = layers.Dense(embed_dim)
        self.ln1 = tf.keras.layers.LayerNormalization(epsilon=1e-5, dtype=tf.float32)
        self.ln2 = tf.keras.layers.LayerNormalization(epsilon=1e-5, dtype=tf.float32)
        self.ln3 = tf.keras.layers.LayerNormalization(epsilon=1e-5, dtype=tf.float32)
    def call(self, x):
        x = self.ln1(x)
        attn = self.mha(x, x, x)
        x = self.ln2(attn) + x
        re = x
        w = self.WB(x)
        a, b = tf.split(w, 2, axis=-1)
        g = tf.nn.silu(a) * b
        o = self.W(g)
        return self.ln3(o) + re

class L2NormLayer(layers.Layer):
    def __init__(self, axis=1, epsilon=1e-10, **kwargs):
        super().__init__(**kwargs)
        self.axis = axis
        self.epsilon = epsilon

    def call(self, inputs):
        return tf.math.l2_normalize(inputs, axis=self.axis, epsilon=self.epsilon)

    def get_config(self):
        return {"axis": self.axis, "epsilon": self.epsilon, **super().get_config()}

class SentenceEncoder(tf.keras.Model):
    def __init__(self, vocab_size, embed_dim=384, latent_dim=384, max_len=128, pad_id=3):
        super().__init__()
        self.pad_id = pad_id
        self.embed = layers.Embedding(vocab_size, embed_dim)
        self.pos_embed = layers.Embedding(input_dim=max_len, output_dim=embed_dim)
        self.blocks = [EncoderBlock(embed_dim=embed_dim, latent_dim=latent_dim) for _ in range(2)]
        self.attn_pool = layers.Dense(1)
        self.ln_f = layers.LayerNormalization(epsilon=1e-5, dtype=tf.float32)
        self.latent = layers.Dense(latent_dim, activation=None)  # tanh 제거
        self.l2norm = L2NormLayer()  # 추가

    def call(self, x):
        positions = tf.range(tf.shape(x)[1])[tf.newaxis, :]
        x_embed = self.embed(x) + self.pos_embed(positions)
        mask = tf.cast(tf.not_equal(x, self.pad_id), tf.float32)

        x = x_embed
        for block in self.blocks:
            x = block(x)
        x = self.ln_f(x)

        scores = self.attn_pool(x)
        scores = tf.where(tf.equal(mask[..., tf.newaxis], 0), -1e9, scores)
        scores = tf.nn.softmax(scores, axis=1)
        pooled = tf.reduce_sum(x * scores, axis=1)

        latent = self.latent(pooled)
        return self.l2norm(latent)  # L2 정규화
# 3️⃣ 모델 로드
# ===============================
encoder = SentenceEncoder(vocab_size=vocab_size)
encoder(np.zeros((1, MAX_LEN), dtype=np.int32))  # 모델 빌드
encoder.load_weights(MODEL_PATH)

# ===============================
# 4️⃣ 벡터화 함수
# ===============================
def get_sentence_vector(sentence):
    tokens = pad_sentence(encode_sentence(sentence))
    vec = encoder(np.array([tokens])).numpy()[0]
    return vec / np.linalg.norm(vec)

# ===============================
# 5️⃣ 가장 비슷한 문장 찾기
# ===============================
def find_most_similar(query, s1, s2, s3):
    candidates = [s1, s2, s3]
    candidate_vectors = np.stack([get_sentence_vector(c) for c in candidates]).astype(np.float32)
    query_vector = get_sentence_vector(query)
    
    sims = candidate_vectors @ query_vector  # cosine similarity
    top_idx = np.argmax(sims)
    
    return {
        "가장 비슷한 문장": candidates[top_idx],
        "유사도": float(sims[top_idx])
    }

# ===============================
# 6️⃣ Gradio UI
# ===============================
with gr.Blocks() as demo:
    gr.Markdown("## 🔍 문장 유사도 검색기 (쿼리 1개 + 후보 3개)")
    with gr.Row():
        query_input = gr.Textbox(label="검색할 문장 (Query)", placeholder="여기에 입력")
    with gr.Row():
        s1_input = gr.Textbox(label="검색 후보 1")
        s2_input = gr.Textbox(label="검색 후보 2")
        s3_input = gr.Textbox(label="검색 후보 3")
    output = gr.JSON(label="결과")
    
    search_btn = gr.Button("가장 비슷한 문장 찾기")
    search_btn.click(
        fn=find_most_similar,
        inputs=[query_input, s1_input, s2_input, s3_input],
        outputs=output
    )

demo.launch()