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120 lines
3.2 KiB
120 lines
3.2 KiB
2 years ago
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"___\n",
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"\n",
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"<a href='http://www.pieriandata.com'><img src='../Pierian_Data_Logo.png'/></a>\n",
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"___\n",
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"<center><em>Copyright by Pierian Data Inc.</em></center>\n",
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"<center><em>For more information, visit us at <a href='http://www.pieriandata.com'>www.pieriandata.com</a></em></center>"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Serving a Model as an API\n",
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"\n",
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"**NOTE: While we show this inside a Jupyter Notebook, you would probably never deploy something as a notebook in a real-world setting. Everything here is in one cell to reflect that this should be a .py file. We also included a duplicate .py file in this folder.**\n",
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"\n",
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"\n",
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"---\n",
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"\n",
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"**NOTE: You will need to install Flask to serve the API: https://flask.palletsprojects.com/en/2.0.x/installation/**\n",
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"\n",
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" pip install flask\n",
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" \n",
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" or\n",
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" \n",
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" conda install flask\n",
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"\n",
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"---\n",
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"\n",
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"\n",
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"## api.py (Run this as a script as shown in the video, NOT from within a Jupyter Cell)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"############################\n",
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"######## IMPORTS ##########\n",
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"##########################\n",
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"from flask import Flask, request, jsonify\n",
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"import joblib\n",
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"import pandas as pd\n",
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"\n",
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"# Create Flask App\n",
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"app = Flask(__name__)\n",
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"\n",
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"\n",
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"# Create API routing call\n",
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"@app.route('/predict', methods=['POST'])\n",
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"def predict():\n",
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" \n",
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" # Get JSON Request\n",
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" feat_data = request.json\n",
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" # Convert JSON request to Pandas DataFrame\n",
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" df = pd.DataFrame(feat_data)\n",
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" # Match Column Na,es\n",
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" df = df.reindex(columns=col_names)\n",
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" # Get prediction\n",
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" prediction = list(model.predict(df))\n",
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" # Return JSON version of Prediction\n",
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" return jsonify({'prediction': str(prediction)})\n",
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"\n",
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" \n",
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"\n",
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"if __name__ == '__main__':\n",
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"\n",
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" # LOADS MODEL AND FEATURE COLUMNS\n",
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" model = joblib.load(\"final_model.pkl\") \n",
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" col_names = joblib.load(\"column_names.pkl\") \n",
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"\n",
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" app.run(debug=True)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# JSON Post Request\n",
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"\n",
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"1. POST to: http://127.0.0.1:5000/predict\n",
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"2. Select Body\n",
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"3. Select Raw\n",
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"4. Select JSON(application/json)\n",
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"5. Supply JSON for Features:\n",
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" [{\"TV\":230.1,\"radio\":37.8,\"newspaper\":69.2}]\n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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