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best_arima_models.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c62c5b809e6ea8ab754b5179f68e77a697800c577df11a61d852d6d7ee974025
+size 5552038

gradiomojo.ipynb

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+{
+  "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"
+      ]
+    }
+  ]
+}