app.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "source": [
        "!pip install gradio"
      ],
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AG3LJPwN8_bz",
        "outputId": "579e2d88-15d8-4500-a6e8-ea10ec00fabf"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[?25hInstalling collected packages: pydub, websockets, tomlkit, semantic-version, ruff, python-multipart, orjson, h11, ffmpy, aiofiles, uvicorn, starlette, httpcore, httpx, fastapi, gradio-client, gradio\n",
            "  Attempting uninstall: tomlkit\n",
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            "    Uninstalling tomlkit-0.13.0:\n",
            "      Successfully uninstalled tomlkit-0.13.0\n",
            "Successfully installed aiofiles-23.2.1 fastapi-0.112.1 ffmpy-0.4.0 gradio-4.41.0 gradio-client-1.3.0 h11-0.14.0 httpcore-1.0.5 httpx-0.27.0 orjson-3.10.7 pydub-0.25.1 python-multipart-0.0.9 ruff-0.6.1 semantic-version-2.10.0 starlette-0.38.2 tomlkit-0.12.0 uvicorn-0.30.6 websockets-12.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 646
        },
        "id": "sk8EestI8YRY",
        "outputId": "d0434b66-0088-4611-88eb-e031e246e264"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Setting queue=True in a Colab notebook requires sharing enabled. Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n",
            "\n",
            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
            "Running on public URL: https://8f53fe8496666cb51a.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<div><iframe src=\"https://8f53fe8496666cb51a.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": []
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "import gradio as gr\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import pickle\n",
        "\n",
        "# Load the trained model from the pickle file\n",
        "with open('best_arima_models.pkl', 'rb') as f:\n",
        "    model = pickle.load(f)\n",
        "\n",
        "def predict_demand(mapped_code, num_months):\n",
        "    try:\n",
        "        # Retrieve the specific model for the mapped code\n",
        "        if mapped_code not in model:\n",
        "            return f\"No model found for Mapped Code: {mapped_code}\"\n",
        "\n",
        "        model_for_code = model[mapped_code]\n",
        "\n",
        "        # Generate a date range for the prediction period\n",
        "        dates = pd.date_range(start=pd.Timestamp.today(), periods=num_months, freq='M')\n",
        "\n",
        "        # Make predictions\n",
        "        future_steps = len(dates)\n",
        "        forecast = model_for_code.forecast(steps=future_steps)\n",
        "\n",
        "        # Prepare a DataFrame for display\n",
        "        df = pd.DataFrame({\n",
        "            'Date': dates.strftime('%Y-%m'),\n",
        "            'Predicted Demand': forecast\n",
        "        })\n",
        "\n",
        "        return df\n",
        "\n",
        "    except Exception as e:\n",
        "        print(f\"Error occurred: {e}\")\n",
        "        return f\"An error occurred: {str(e)}\"\n",
        "\n",
        "# Gradio Interface Definition\n",
        "gr.Interface(\n",
        "    fn=predict_demand,\n",
        "    inputs=[\n",
        "        gr.Textbox(label=\"Mapped Code\", placeholder=\"Enter mapped code here\"),\n",
        "        gr.Slider(minimum=1, maximum=12, step=1, label=\"Number of Months\")\n",
        "    ],\n",
        "    outputs=[\n",
        "        gr.Dataframe(label=\"Predicted Demand\")\n",
        "    ],\n",
        "    title=\"Demand Forecasting\",\n",
        "    description=\"Enter the mapped code and the number of months to predict future demand.\"\n",
        ").launch()\n"
      ]
    }
  ]
}