From c9a501ae5492df248778bf66f330cd36a0887f60 Mon Sep 17 00:00:00 2001
From: ehddnr301 <dy950328@gmail.com>
Date: Mon, 13 Sep 2021 10:59:28 +0900
Subject: [PATCH] Change modeling OOP
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모델을 load하는것과 predict하는 부분을 class로 만들어서
좀더 객체지향적 프로그래밍을 하고자 하였습니다.
---
 app/api/router/predict.py | 38 ++++++++--------------
 app/utils.py              | 68 +++++++++++++++++++++++----------------
 2 files changed, 55 insertions(+), 51 deletions(-)

diff --git a/app/api/router/predict.py b/app/api/router/predict.py
index 136c5a0..9c72462 100644
--- a/app/api/router/predict.py
+++ b/app/api/router/predict.py
@@ -1,15 +1,14 @@
 # -*- coding: utf-8 -*-
-import codecs
 import numpy as np
-import pickle
 from typing import List
 
-from fastapi import APIRouter, HTTPException
+from fastapi import APIRouter
+from starlette.concurrency import run_in_threadpool
 
 from app import models
 from app.api.schemas import ModelCorePrediction
 from app.database import engine
-from app.utils import my_model
+from app.utils import ScikitLearnModel, my_model
 
 
 models.Base.metadata.create_all(bind=engine)
@@ -23,7 +22,7 @@
 
 
 @router.put("/insurance")
-def predict_insurance(info: ModelCorePrediction, model_name: str):
+async def predict_insurance(info: ModelCorePrediction, model_name: str):
     """
     Get information and predict insurance fee
     param:
@@ -39,30 +38,21 @@ def predict_insurance(info: ModelCorePrediction, model_name: str):
     return:
         insurance_fee: float
     """
-    query = """
-        SELECT model_file
-        FROM model_core
-        WHERE model_name='{}';
-    """.format(model_name)
 
-    reg_model = engine.execute(query).fetchone()
+    def sync_call(info, model_name):
+        model = ScikitLearnModel(model_name)
+        model.load_model()
 
-    if reg_model is None:
-        raise HTTPException(
-            status_code=404,
-            detail="Model Not Found",
-            headers={"X-Error": "Model Not Found"},
-        )
+        info = info.dict()
+        test_set = np.array([*info.values()]).reshape(1, -1)
 
-    loaded_model = pickle.loads(
-        codecs.decode(reg_model[0], 'base64'))
+        pred = model.predict_target(test_set)
 
-    info = info.dict()
-    test_set = np.array([*info.values()]).reshape(1, -1)
+        return {"result": pred.tolist()[0]}
 
-    pred = loaded_model.predict(test_set)
+    result = await run_in_threadpool(sync_call, info, model_name)
 
-    return {"result": pred.tolist()[0]}
+    return result
 
 
 @router.put("/atmos")
@@ -71,7 +61,7 @@ async def predict_temperature(time_series: List[float]):
         return "time series must have 72 values"
 
     try:
-        tf_model = my_model.my_model
+        tf_model = my_model.model
         time_series = np.array(time_series).reshape(1, -1, 1)
         result = tf_model.predict(time_series)
         return result.tolist()
diff --git a/app/utils.py b/app/utils.py
index 45d2ced..1f9b2e7 100644
--- a/app/utils.py
+++ b/app/utils.py
@@ -11,46 +11,60 @@
 
 base_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
 
-# physical_devices = tf.config.list_physical_devices('GPU')
-# tf.config.experimental.set_memory_growth(physical_devices[0], enable=True)
+physical_devices = tf.config.list_physical_devices('GPU')
+if physical_devices:
+    tf.config.experimental.set_memory_growth(physical_devices[0], enable=True)
 
 
-class MyModel:
-    def __init__(self):
-        self._my_model = None
+class CoreModel:
 
-    def load_tf_model(self, model_name):
-        """
-        * DB에 있는 텐서플로우 모델을 불러옵니다. 
-        * 모델은 zip형식으로 압축되어 binary로 저장되어 있습니다.
-        * 모델의 이름을 받아 압축 해제 및 tf_model폴더 아래에 저장한 후 로드하여 
-          텐서플로우 모델 객체를 반환합니다.
-        """
+    def __init__(self, model_name):
+        self.model_name = model_name
+        self.model = None
+        self.query = """
+                SELECT model_file
+                FROM model_core
+                WHERE model_name='{}';
+            """.format(self.model_name)
 
-        query = f"""SELECT model_file
-                    FROM model_core
-                    WHERE model_name='{model_name}';"""
+    def load_model(self):
+        raise Exception
 
-        bin_data = engine.execute(query).fetchone()[0]
+    def predict_target(self, target_data):
+        return self.model.predict(target_data)
 
-        model_buffer = pickle.loads(codecs.decode(bin_data, "base64"))
-        model_path = os.path.join(base_dir, "tf_model", model_name)
 
-        with zipfile.ZipFile(model_buffer, "r") as bf:
-            bf.extractall(model_path)
-        tf_model = tf.keras.models.load_model(model_path)
+class ScikitLearnModel(CoreModel):
+    def __init__(self, *args):
+        super().__init__(*args)
+
+    def load_model(self):
+        _model = engine.execute(self.query).fetchone()
+        if _model is None:
+            raise ValueError('Model Not Found!')
 
-        return tf_model
+        self.model = pickle.loads(
+            codecs.decode(_model[0], 'base64')
+        )
+
+
+class TensorFlowModel(CoreModel):
+    def __init__(self, *args):
+        super().__init__(*args)
 
     def load_model(self):
-        self._my_model = self.load_tf_model('test_model')
+        _model = engine.execute(self.query).fetchone()
+        if _model is None:
+            raise ValueError('Model Not Found!')
+        model_buffer = pickle.loads(codecs.decode(_model[0], "base64"))
+        model_path = os.path.join(base_dir, "tf_model", self.model_name)
 
-    @property
-    def my_model(self):
-        return self._my_model
+        with zipfile.ZipFile(model_buffer, "r") as bf:
+            bf.extractall(model_path)
+        self.model = tf.keras.models.load_model(model_path)
 
 
-my_model = MyModel()
+my_model = TensorFlowModel('test_model')
 my_model.load_model()