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diff --git a/knn.py b/knn.py
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+++ b/knn.py
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+# knn.py
+import numpy as np
+import pandas as pd
+from pathlib import Path
+from collections import defaultdict
+
+EPS = 1e-12
+
+
+def l2_normalize(x: np.ndarray):
+    # 벡터 정규화 불가 (NaN, 0벡터)
+    n = np.linalg.norm(x)
+    if not np.isfinite(n) or n < EPS:
+        return None
+    return x / n
+
+
+class FullKNN:
+    def __init__(self, embeddings_path: str):
+        
+        # 임베딩 데이터 로드
+        df = pd.read_parquet(Path(embeddings_path))
+
+        # 임베딩 벡터 행렬화
+        X = np.vstack(df["vector"].apply(lambda v: np.asarray(v, np.float32)))
+        norms = np.linalg.norm(X, axis=1)
+
+        # NaN / zero-norm 벡터 제거
+        ok = np.isfinite(X).all(axis=1) & (norms > EPS)
+        df = df.loc[ok].reset_index(drop=True)
+        X = X[ok] / norms[ok][:, None]
+
+        # 임베딩 및 메타데이터 저장
+        self.X = X
+        self.gender = df["gender"].to_numpy()
+        self.animal = df["animal_type"].to_numpy()
+
+        # 성별별 인덱스 미리 구성
+        self.idx_by_gender = {
+            g: np.where(self.gender == g)[0]
+            for g in np.unique(self.gender)
+        }
+
+    def predict(self, user_vec: np.ndarray, gender: str, K: int = 50):
+        
+        # 성별 데이터 x 중단
+        if gender not in self.idx_by_gender:
+            return []
+
+        # 사용자 임베딩 정규화
+        u = l2_normalize(np.asarray(user_vec, np.float32))
+        if u is None:
+            return []
+
+        # 성별에 해당하는 임베딩만 사용
+        idx = self.idx_by_gender[gender]
+        Xg = self.X[idx]
+        ag = self.animal[idx]
+
+        # 코사인 유사도 계산
+        sims = Xg @ u
+
+        # top-K 이웃 선택
+        K = min(K, len(sims))
+        top_idx = np.argpartition(-sims, K - 1)[:K]
+
+        # 동물상별 가중 투표 (유사도 합산)
+        scores = defaultdict(float)
+        for i in top_idx:
+            s = sims[i]
+            if s > 0:
+                scores[ag[i]] += float(s)
+
+        if not scores:
+            return []
+
+        # 가장 점수가 높은 동물상 반환
+        best = max(scores.items(), key=lambda x: x[1])
+        return [{"animal_type": best[0], "score": best[1]}]
diff --git a/main.py b/main.py
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--- /dev/null
+++ b/main.py
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+# main.py
+import numpy as np
+from fastapi import FastAPI, UploadFile, File, Form, HTTPException
+from insightface.app import FaceAnalysis
+
+from knn import FullKNN
+from preprocess import biggest_face, safe_norm_vec, decode_upload_image
+
+
+# ===== 설정 =====
+EMB_PATH = "./artifacts/embeddings_fixed.parquet"
+DET_SIZE = (640, 640)
+
+
+# ===== 서버 시작 시 로딩 =====
+face_app = FaceAnalysis(name="buffalo_l", providers=["CPUExecutionProvider"])
+face_app.prepare(ctx_id=0, det_size=DET_SIZE)
+
+knn = FullKNN(EMB_PATH)
+
+app = FastAPI()
+
+
+@app.post("/analyze")
+async def analyze(
+    gender: str = Form(...),
+    file: UploadFile = File(...),
+):
+    # 남녀 여부
+    if gender not in ("남자", "여자"):
+        raise HTTPException(400, "gender must be 남자 or 여자")
+
+    # 사진 여부
+    img_bytes = await file.read()
+    img = decode_upload_image(img_bytes)
+    if img is None:
+        raise HTTPException(400, "invalid image")
+
+    # 얼굴 여부
+    faces = face_app.get(img)
+    if not faces:
+        raise HTTPException(422, "no face detected")
+
+    f = biggest_face(faces)
+
+    # 얼굴 선명도
+    det_score = float(getattr(f, "det_score", 0.0))
+    if det_score < 0.5:
+        raise HTTPException(422, "face too unclear")
+
+    # 임베딩 불가
+    vec = safe_norm_vec(np.asarray(f.embedding, dtype=np.float32))
+    if vec is None:
+        raise HTTPException(422, "invalid embedding")
+
+    # === knn ===
+    result = knn.predict(
+        user_vec=vec,
+        gender=gender,
+        K=50
+    )
+
+    if not result:
+        raise HTTPException(422, "no prediction")
+
+    return {
+        "animal_type": result[0]["animal_type"],
+        "det_score": det_score,
+    }
diff --git a/preprocess.py b/preprocess.py
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index 0000000..cbbd013
--- /dev/null
+++ b/preprocess.py
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+# preprocess.py
+import numpy as np
+import cv2
+
+
+def biggest_face(faces):
+    # 여러 얼굴 중 가장 큰 얼굴 선택
+    return max(
+        faces,
+        key=lambda f: (f.bbox[2] - f.bbox[0]) * (f.bbox[3] - f.bbox[1])
+    )
+
+
+def safe_norm_vec(vec: np.ndarray):
+    # 임베딩 벡터 정규화 (NaN / 0벡터 방어)
+    v = np.asarray(vec, dtype=np.float32)
+    n = np.linalg.norm(v)
+    if not np.isfinite(n) or n < 1e-12:
+        return None
+    return v / n
+
+
+def decode_upload_image(img_bytes: bytes):
+    # 업로드된 이미지 byte → OpenCV 이미지 변환
+    img = cv2.imdecode(np.frombuffer(img_bytes, np.uint8), cv2.IMREAD_COLOR)
+    return img