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app.py

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+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()

requirements.txt

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+numpy
+tensorflow
+sentencepiece
+gradio
+requests