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rizalkun · Nov 25, 2019 · 956ce8f · 956ce8f
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diff --git a/.DS_Store b/.DS_Store
diff --git a/.gitignore b/.gitignore
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+.vscode
+.idea
+env
+science
diff --git a/README.md b/README.md
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+# example-datascience
diff --git a/__init__.py b/__init__.py
diff --git a/__pycache__/Hello.cpython-37.pyc b/__pycache__/Hello.cpython-37.pyc
diff --git a/__pycache__/app.cpython-37.pyc b/__pycache__/app.cpython-37.pyc
diff --git a/__pycache__/config.cpython-37.pyc b/__pycache__/config.cpython-37.pyc
diff --git a/__pycache__/db.cpython-37.pyc b/__pycache__/db.cpython-37.pyc
diff --git a/__pycache__/run.cpython-37.pyc b/__pycache__/run.cpython-37.pyc
diff --git a/app.py b/app.py
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+from flask import Blueprint
+from flask_restful import Api
+from resources.Sales import Popularity
+
+api_bp = Blueprint('api', __name__)
+api = Api(api_bp)
+
+# Route
+api.add_resource(Popularity, '/sales/popularity')
diff --git a/requirements.txt b/requirements.txt
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+flask
+flask_restful
+flask_mysql
diff --git a/resources/Sales.py b/resources/Sales.py
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+from flask import jsonify, render_template, make_response
+from flask_restful import Resource
+import pandas as pd
+import numpy as np
+import time
+import turicreate as tc
+import sqlalchemy as sql
+from sklearn.model_selection import train_test_split
+import json
+
+def load_data():
+        try:
+            connect_string = 'mysql://root:[email protected]:3306/example_science'
+            sql_engine = sql.create_engine(connect_string)
+            query = "select * from Transactions"
+            return pd.read_sql_query(query, sql_engine)
+        except Exception as e:
+            print(e)
+
+def model(train_data, name, user_id, item_id, target, n_rec):
+    if name == 'popularity':
+        model = tc.popularity_recommender.create(tc.SFrame(train_data), 
+                                                    user_id=user_id, 
+                                                    item_id=item_id, 
+                                                    target=target)
+    elif name == 'cosine':
+        model = tc.item_similarity_recommender.create(train_data, 
+                                                    user_id=user_id, 
+                                                    item_id=item_id, 
+                                                    target=target, 
+                                                    similarity_type='cosine')
+    elif name == 'pearson':
+        model = tc.item_similarity_recommender.create(train_data, 
+                                                    user_id=user_id, 
+                                                    item_id=item_id, 
+                                                    target=target, 
+                                                    similarity_type='pearson')
+
+    recom = model.recommend(k=n_rec)
+    return recom
+
+def split_data(data):
+    train, test = train_test_split(data, test_size = .2)
+    train_data = train
+    return train_data
+
+class Popularity(Resource):
+    def get(self):
+        try:
+            transactions = load_data()
+            data = pd.melt(transactions.set_index('customerId')['product_name'].apply(pd.Series).reset_index(),
+            id_vars=['customerId'],
+            value_name='product_name').groupby(['customerId', 'product_name']) \
+                .agg({'product_name' : 'count'}) \
+                .rename(columns={'product_name' : 'product_count'}) \
+                .reset_index() \
+                .sort_values(by='product_count', ascending=False)
+
+            name = 'popularity'
+            user_id = 'customerId'
+            item_id = 'product_name'
+            target = 'product_count'
+            n_rec = 10 # number of items to recommend
+            train_data = split_data(data)
+            result = model(train_data, name, user_id, item_id, target, n_rec)
+            df_result = pd.DataFrame(result)
+            headers = {'Content-Type': 'text/html'}
+            return make_response(render_template('view.html', tables=[df_result.to_html(classes='data', header="true")]),200,headers)
+        except Exception as e:
+            print(e)
+            return { "success" : False, "message" : "Internal server error" }
+
diff --git a/resources/__init__.py b/resources/__init__.py
diff --git a/resources/__pycache__/Hello.cpython-37.pyc b/resources/__pycache__/Hello.cpython-37.pyc
diff --git a/resources/__pycache__/Sales.cpython-37.pyc b/resources/__pycache__/Sales.cpython-37.pyc
diff --git a/resources/__pycache__/__init__.cpython-37.pyc b/resources/__pycache__/__init__.cpython-37.pyc
diff --git a/run.py b/run.py
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+from flask import Flask
+
+def create_app(config_filename):
+    app = Flask(__name__)
+
+    from app import api_bp
+    app.register_blueprint(api_bp, url_prefix='/api')
+
+    return app
+
+
+if __name__ == "__main__":
+    app = create_app("config")
+    app.run(debug=True)
diff --git a/templates/view.html b/templates/view.html
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+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <title>Popularity</title>
+</head>
+<body>
+
+{% for table in tables %}
+            {{ table|safe }}
+{% endfor %}
+</body>
+</html>
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+Diff line change
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+    .vscode
+    .idea
+    env
+    science