Testing nb viewer, local to online jupyter notebook

test-nb-viewer.ipynb

{
  "metadata": {
    "language_info": {
      "name": "python",
      "version": "3.8"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 4,
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "source": [
        "%%writefile iris_classifier.py",
        "#writing script for BentoML Service Class",
        "from bentoml import env, artifacts, api, BentoService",
        "from bentoml.artifact import SklearnModelArtifact",
        "from bentoml.adapters import DataframeInput",
        "@env(auto_pip_dependencies=True, conda_dependencies=['bentoml'])",
        "@artifacts([SklearnModelArtifact('model')])",
        "class IrisClassifier(BentoService):",
        "    @api(input=DataframeInput(), batch=True)",
        "    def predict(self, df):",
        "        return self.artifacts.model.predict(df)"
      ],
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Overwriting iris_classifier.py\n"
          ]
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "source": [
        "import pandas as pd",
        "import matplotlib.pyplot as plt",
        "data = {",
        "    'Product': ['A', 'B', 'C', 'D'],",
        "    'Sales': [1000, 1500, 800, 950]",
        "}",
        "df = pd.DataFrame(data)",
        "total_sales = df['Sales'].sum()",
        "print(f'Total Sales: ${total_sales}')",
        "plt.figure(figsize=(10, 6))",
        "plt.bar(df['Product'], df['Sales'], color=['blue', 'green', 'red', 'cyan'])",
        "plt.title('Sales by Product')",
        "plt.xlabel('Product')",
        "plt.ylabel('Sales ($)')",
        "plt.show()"
      ],
      "outputs": [],
      "metadata": {}
    }
  ]
}