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diff --git a/24.不能忽視的過擬合與欠擬合/README.html b/24.不能忽視的過擬合與欠擬合/README.html
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diff --git a/26.交叉驗證 K-Fold Cross-Validation/README.html b/26.交叉驗證 K-Fold Cross-Validation/README.html
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diff --git a/27.機器學習常犯錯的十件事/README.html b/27.機器學習常犯錯的十件事/README.html
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diff --git a/28.儲存訓練好的模型/28.XGBoost(Classification-iris).ipynb b/28.儲存訓練好的模型/28.XGBoost(Classification-iris).ipynb
diff --git a/28.儲存訓練好的模型/README.html b/28.儲存訓練好的模型/README.html
diff --git a/28.儲存訓練好的模型/image/img28-1.png b/28.儲存訓練好的模型/image/img28-1.png
diff --git a/28.儲存訓練好的模型/image/img28-2.png b/28.儲存訓練好的模型/image/img28-2.png
diff --git a/28.儲存訓練好的模型/xgboost-iris.pgz b/28.儲存訓練好的模型/xgboost-iris.pgz
diff --git a/28.儲存訓練好的模型/xgboost-iris.pickle b/28.儲存訓練好的模型/xgboost-iris.pickle
diff --git a/29.使用Python-Flask架設API吧/Flask-API-example-with-ML-model/model.py b/29.使用Python-Flask架設API吧/Flask-API-example-with-ML-model/model.py
@@ -0,0 +1,11 @@
+# -*- coding: UTF-8 -*-
+import pickle
+import gzip
+
+# 載入Model
+with gzip.open('./model/xgboost-iris.pgz', 'rb') as f:
+    xgboostModel = pickle.load(f)
+
+def predict(input):
+    pred=xgboostModel.predict(input)[0]
+    return pred
diff --git a/29.使用Python-Flask架設API吧/Flask-API-example-with-ML-model/model/xgboost-iris.pgz b/29.使用Python-Flask架設API吧/Flask-API-example-with-ML-model/model/xgboost-iris.pgz
diff --git a/29.使用Python-Flask架設API吧/Flask-API-example-with-ML-model/requirements.txt b/29.使用Python-Flask架設API吧/Flask-API-example-with-ML-model/requirements.txt
@@ -0,0 +1,5 @@
+Flask
+Flask-Cors
+numpy
+scikit-learn
+xgboost
diff --git a/29.使用Python-Flask架設API吧/Flask-API-example-with-ML-model/run.py b/29.使用Python-Flask架設API吧/Flask-API-example-with-ML-model/run.py
@@ -0,0 +1,31 @@
+# -*- coding: UTF-8 -*-
+import numpy as np
+import model
+
+from flask import Flask, request, jsonify
+from flask_cors import CORS
+
+app = Flask(__name__)
+CORS(app)
+
+@app.route('/')
+def index():
+    return 'hello!!'
+
+@app.route('/predict', methods=['POST'])
+def postInput():
+    # 取得前端傳過來的數值
+    insertValues = request.get_json()
+    x1=insertValues['sepalLengthCm']
+    x2=insertValues['sepalWidthCm']
+    x3=insertValues['petalLengthCm']
+    x4=insertValues['petalWidthCm']
+    input = np.array([[x1, x2, x3, x4]])
+
+    result = model.predict(input)
+
+    return jsonify({'return': str(result)})
+
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=3000, debug=True)