Delete gradiomojo.ipynb

gradiomojo.ipynb

@@ -1,115 +0,0 @@
-{
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "provenance": []
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    }
-  },
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": 5,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 646
-        },
-        "id": "Y2i3qMAayerW",
-        "outputId": "09a4d84b-3aab-4886-b5d4-948f48ca5017"
-      },
-      "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://abaebd2aaded04d1bb.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://abaebd2aaded04d1bb.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": 5
-        }
-      ],
-      "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"
-      ]
-    }
-  ]
-}