Webapp_20220410.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6rLGtZbqFFOp",
        "outputId": "53ff0845-1e63-46ec-9ca6-cbb2c03afc2c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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          ]
        }
      ],
      "source": [
        "pip install streamlit"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qAMZ0Jks44V6",
        "outputId": "264aab7b-8542-4a05-ce91-85c2a778300d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Overwriting app.py\n"
          ]
        }
      ],
      "source": [
        "%%writefile app.py\n",
        "import streamlit as st\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from fbprophet.plot import plot_plotly\n",
        "from plotly import graph_objs as go\n",
        "from sklearn import preprocessing\n",
        "from sklearn.model_selection import train_test_split\n",
        "from xgboost import XGBRegressor\n",
        "from dateutil.relativedelta import relativedelta\n",
        "\n",
        "oildf = pd.read_html(\"https://www.indexmundi.com/commodities/?commodity=crude-oil-brent&months=360\")[1]\n",
        "coaldf = pd.read_html(\"https://www.indexmundi.com/commodities/?commodity=coal-australian&months=360\")[1]\n",
        "gasdf = pd.read_html(\"https://www.indexmundi.com/commodities/?commodity=natural-gas&months=360\")[4]\n",
        "sugardf = pd.read_html(\"https://www.indexmundi.com/commodities/?commodity=sugar&months=360\")[4]\n",
        "oredf = pd.read_html(\"https://www.indexmundi.com/commodities/?commodity=iron-ore&months=360\")[1]\n",
        "copperdf = pd.read_html(\"https://www.indexmundi.com/commodities/?commodity=copper&months=360\")[4]\n",
        "oildf.drop('Change', inplace = True, axis = 1)\n",
        "coaldf.drop('Change', inplace = True, axis = 1)\n",
        "gasdf.drop('Change', inplace = True, axis = 1)\n",
        "sugardf.drop('Change', inplace = True, axis = 1)\n",
        "oredf.drop('Change', inplace = True, axis = 1)\n",
        "copperdf.drop('Change', inplace = True, axis = 1)\n",
        "oildf.rename(columns = {'Price': 'oil_price'}, inplace = True)\n",
        "coaldf.rename(columns = {'Price': 'coal_price'}, inplace = True)\n",
        "gasdf.rename(columns = {'Price': 'gas_price'}, inplace = True)\n",
        "sugardf.rename(columns = {'Price': 'sugar_price'}, inplace = True)\n",
        "oredf.rename(columns = {'Price': 'ore_price'}, inplace = True)\n",
        "copperdf.rename(columns = {'Price': 'copper_price'}, inplace = True)\n",
        "df = (oildf.merge(coaldf).merge(gasdf).merge(sugardf).merge(oredf).merge(copperdf))\n",
        "df.index = pd.to_datetime(df['Month'], format='%b %Y')\n",
        "df.drop('Month', inplace = True, axis = 1)\n",
        "odf = df.copy()\n",
        "\n",
        "st.title('Commodity Price Forecast App')\n",
        "commo = ('oil_price', 'coal_price', 'gas_price', 'sugar_price', 'ore_price', 'copper_price')\n",
        "target = st.selectbox('Select item for forecast', commo)\n",
        "\n",
        "data_load_state = st.text('Loading data...')\n",
        "data = df[target]\n",
        "data_load_state.text('Loading data... done!')\n",
        "\n",
        "st.subheader('Raw data')\n",
        "st.write(data)\n",
        "\n",
        "# Plot raw data\n",
        "def plot_raw_data():\n",
        "\tfig = go.Figure()\n",
        "\tfig.add_trace(go.Scatter(x=df.index, y=df[target]))\n",
        "\tfig.layout.update(title_text='Historical Price Curve', xaxis_rangeslider_visible=True)\n",
        "\tst.plotly_chart(fig)\n",
        "\t\n",
        "plot_raw_data()\n",
        "\n",
        "n_months = st.slider('Number of months for prediction:', 1, 4)\n",
        "\n",
        "# Predict forecast\n",
        "forecast_out = n_months\n",
        "df['Y'] = df[target].shift(-forecast_out)\n",
        "X = np.array(df.drop(['Y'], 1))\n",
        "X = preprocessing.scale(X)\n",
        "X_lately = X[-forecast_out:]\n",
        "X = X[:-forecast_out]\n",
        "y = np.array(df['Y'])\n",
        "y = y[:-forecast_out]\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)\n",
        "clf = XGBRegressor(learning_rate = 0.1, max_depth = 3, n_estimators = 200, objective = 'reg:squarederror')\n",
        "clf.fit(X_train, y_train)\n",
        "\n",
        "forecast_set = clf.predict(X_lately)\n",
        "df['Forecast'] = np.nan\n",
        "for i in forecast_set:\n",
        "    next_date = df.iloc[-1].name + relativedelta(months=1)\n",
        "    df.loc[next_date] = [np.nan for _ in range(len(df.columns)-1)]+[i]\n",
        "\n",
        "# Show and plot forecast\n",
        "st.subheader('Forecast data')\n",
        "st.write(df[df['Forecast'].notnull()]['Forecast'])\n",
        "\n",
        "st.write(f'Forecast plot for {n_months} months')\n",
        "def plot_forecast():\n",
        "\tfig = go.Figure()\n",
        "\tfig.add_trace(go.Scatter(x=df.index, y=df[target]))\n",
        "\tfig.add_trace(go.Scatter(x=df.index, y=df['Forecast']))\n",
        "\tfig.layout.update(title_text='Forecast Graph', xaxis_rangeslider_visible=True)\n",
        "\tst.plotly_chart(fig)\n",
        "\t\n",
        "plot_forecast()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "GS96CbvH598p",
        "outputId": "78612849-d7fa-42b6-889f-1c1a7e899dfb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2022-04-17 03:01:30.827 INFO    numexpr.utils: NumExpr defaulting to 2 threads.\n",
            "\u001b[0m\n",
            "\u001b[34m\u001b[1m  You can now view your Streamlit app in your browser.\u001b[0m\n",
            "\u001b[0m\n",
            "\u001b[34m  Network URL: \u001b[0m\u001b[1mhttp://172.28.0.2:8501\u001b[0m\n",
            "\u001b[34m  External URL: \u001b[0m\u001b[1mhttp://35.237.50.211:8501\u001b[0m\n",
            "\u001b[0m\n",
            "\u001b[K\u001b[?25hnpx: installed 22 in 5.099s\n",
            "your url is: https://black-rat-57.loca.lt\n",
            "/content/app.py:59: FutureWarning:\n",
            "\n",
            "In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n",
            "\n",
            "/content/app.py:59: FutureWarning:\n",
            "\n",
            "In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!streamlit run app.py & npx localtunnel --port 8501"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "Webapp_20220410.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}