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from flask import Flask, request, redirect, url_for, flash, jsonify | |
import numpy as np | |
import pickle as p | |
import json | |
app = Flask(__name__) | |
@app.route('/api/', methods=['POST']) | |
def makecalc(): | |
data = request.get_json() | |
prediction = np.array2string(model.predict(data)) | |
return jsonify(prediction) | |
if __name__ == '__main__': | |
modelfile = 'final_prediction.pickle' | |
model = p.load(open(modelfile, 'rb')) | |
app.run(debug=True, host='0.0.0.0') |
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{ | |
"cells": [ | |
{ | |
"cell_type": "code", | |
"execution_count": 1, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"import numpy as np\n", | |
"import pandas as pd\n", | |
"from sklearn import tree\n", | |
"from sklearn.datasets import load_wine\n", | |
"from sklearn.metrics import accuracy_score" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 2, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"wine = load_wine()" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 3, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/html": [ | |
"<div>\n", | |
"<style scoped>\n", | |
" .dataframe tbody tr th:only-of-type {\n", | |
" vertical-align: middle;\n", | |
" }\n", | |
"\n", | |
" .dataframe tbody tr th {\n", | |
" vertical-align: top;\n", | |
" }\n", | |
"\n", | |
" .dataframe thead th {\n", | |
" text-align: right;\n", | |
" }\n", | |
"</style>\n", | |
"<table border=\"1\" class=\"dataframe\">\n", | |
" <thead>\n", | |
" <tr style=\"text-align: right;\">\n", | |
" <th></th>\n", | |
" <th>alcohol</th>\n", | |
" <th>malic_acid</th>\n", | |
" <th>ash</th>\n", | |
" <th>alcalinity_of_ash</th>\n", | |
" <th>magnesium</th>\n", | |
" <th>total_phenols</th>\n", | |
" <th>flavanoids</th>\n", | |
" <th>nonflavanoid_phenols</th>\n", | |
" <th>proanthocyanins</th>\n", | |
" <th>color_intensity</th>\n", | |
" <th>hue</th>\n", | |
" <th>od280/od315_of_diluted_wines</th>\n", | |
" <th>proline</th>\n", | |
" <th>target</th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" <tr>\n", | |
" <th>0</th>\n", | |
" <td>14.23</td>\n", | |
" <td>1.71</td>\n", | |
" <td>2.43</td>\n", | |
" <td>15.6</td>\n", | |
" <td>127.0</td>\n", | |
" <td>2.80</td>\n", | |
" <td>3.06</td>\n", | |
" <td>0.28</td>\n", | |
" <td>2.29</td>\n", | |
" <td>5.64</td>\n", | |
" <td>1.04</td>\n", | |
" <td>3.92</td>\n", | |
" <td>1065.0</td>\n", | |
" <td>0.0</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>1</th>\n", | |
" <td>13.20</td>\n", | |
" <td>1.78</td>\n", | |
" <td>2.14</td>\n", | |
" <td>11.2</td>\n", | |
" <td>100.0</td>\n", | |
" <td>2.65</td>\n", | |
" <td>2.76</td>\n", | |
" <td>0.26</td>\n", | |
" <td>1.28</td>\n", | |
" <td>4.38</td>\n", | |
" <td>1.05</td>\n", | |
" <td>3.40</td>\n", | |
" <td>1050.0</td>\n", | |
" <td>0.0</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>2</th>\n", | |
" <td>13.16</td>\n", | |
" <td>2.36</td>\n", | |
" <td>2.67</td>\n", | |
" <td>18.6</td>\n", | |
" <td>101.0</td>\n", | |
" <td>2.80</td>\n", | |
" <td>3.24</td>\n", | |
" <td>0.30</td>\n", | |
" <td>2.81</td>\n", | |
" <td>5.68</td>\n", | |
" <td>1.03</td>\n", | |
" <td>3.17</td>\n", | |
" <td>1185.0</td>\n", | |
" <td>0.0</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>3</th>\n", | |
" <td>14.37</td>\n", | |
" <td>1.95</td>\n", | |
" <td>2.50</td>\n", | |
" <td>16.8</td>\n", | |
" <td>113.0</td>\n", | |
" <td>3.85</td>\n", | |
" <td>3.49</td>\n", | |
" <td>0.24</td>\n", | |
" <td>2.18</td>\n", | |
" <td>7.80</td>\n", | |
" <td>0.86</td>\n", | |
" <td>3.45</td>\n", | |
" <td>1480.0</td>\n", | |
" <td>0.0</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>4</th>\n", | |
" <td>13.24</td>\n", | |
" <td>2.59</td>\n", | |
" <td>2.87</td>\n", | |
" <td>21.0</td>\n", | |
" <td>118.0</td>\n", | |
" <td>2.80</td>\n", | |
" <td>2.69</td>\n", | |
" <td>0.39</td>\n", | |
" <td>1.82</td>\n", | |
" <td>4.32</td>\n", | |
" <td>1.04</td>\n", | |
" <td>2.93</td>\n", | |
" <td>735.0</td>\n", | |
" <td>0.0</td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
"</table>\n", | |
"</div>" | |
], | |
"text/plain": [ | |
" alcohol malic_acid ash alcalinity_of_ash magnesium total_phenols \\\n", | |
"0 14.23 1.71 2.43 15.6 127.0 2.80 \n", | |
"1 13.20 1.78 2.14 11.2 100.0 2.65 \n", | |
"2 13.16 2.36 2.67 18.6 101.0 2.80 \n", | |
"3 14.37 1.95 2.50 16.8 113.0 3.85 \n", | |
"4 13.24 2.59 2.87 21.0 118.0 2.80 \n", | |
"\n", | |
" flavanoids nonflavanoid_phenols proanthocyanins color_intensity hue \\\n", | |
"0 3.06 0.28 2.29 5.64 1.04 \n", | |
"1 2.76 0.26 1.28 4.38 1.05 \n", | |
"2 3.24 0.30 2.81 5.68 1.03 \n", | |
"3 3.49 0.24 2.18 7.80 0.86 \n", | |
"4 2.69 0.39 1.82 4.32 1.04 \n", | |
"\n", | |
" od280/od315_of_diluted_wines proline target \n", | |
"0 3.92 1065.0 0.0 \n", | |
"1 3.40 1050.0 0.0 \n", | |
"2 3.17 1185.0 0.0 \n", | |
"3 3.45 1480.0 0.0 \n", | |
"4 2.93 735.0 0.0 " | |
] | |
}, | |
"execution_count": 3, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"data = pd.DataFrame(data= np.c_[wine['data'], wine['target']],\n", | |
" columns= wine['feature_names'] + ['target'])\n", | |
"data.head()" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 4, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"X_train = data[:-20]\n", | |
"X_test = data[-20:]\n", | |
"\n", | |
"y_train = X_train.target\n", | |
"y_test = X_test.target\n", | |
"\n", | |
"X_train = X_train.drop('target',1)\n", | |
"X_test = X_test.drop('target',1)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 5, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"clf = tree.DecisionTreeClassifier()\n", | |
"clf = clf.fit(X_train, y_train)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 6, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"y_pred = clf.predict(X_test)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 7, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"accuracy_score: 0.90\n" | |
] | |
} | |
], | |
"source": [ | |
"print(\"accuracy_score: %.2f\"\n", | |
" % accuracy_score(y_test, y_pred))" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Wow, good accuracy, let's create an API!" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 8, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"import pickle\n", | |
"pickle.dump(clf, open('models/final_prediction.pickle', 'wb'))" | |
] | |
} | |
], | |
"metadata": { | |
"kernelspec": { | |
"display_name": "Python 3", | |
"language": "python", | |
"name": "python3" | |
}, | |
"language_info": { | |
"codemirror_mode": { | |
"name": "ipython", | |
"version": 3 | |
}, | |
"file_extension": ".py", | |
"mimetype": "text/x-python", | |
"name": "python", | |
"nbconvert_exporter": "python", | |
"pygments_lexer": "ipython3", | |
"version": "3.6.5" | |
} | |
}, | |
"nbformat": 4, | |
"nbformat_minor": 2 | |
} |
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Learn more about bidirectional Unicode characters
import requests | |
import json | |
url = 'http://0.0.0.0:5000/api/' | |
data = [[14.34, 1.68, 2.7, 25.0, 98.0, 2.8, 1.31, 0.53, 2.7, 13.0, 0.57, 1.96, 660.0]] | |
j_data = json.dumps(data) | |
headers = {'content-type': 'application/json', 'Accept-Charset': 'UTF-8'} | |
r = requests.post(url, data=j_data, headers=headers) | |
print(r, r.text) |
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