{ "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": [ "Collecting gradio\n", " Downloading gradio-4.41.0-py3-none-any.whl.metadata (15 kB)\n", "Collecting aiofiles<24.0,>=22.0 (from gradio)\n", " Downloading aiofiles-23.2.1-py3-none-any.whl.metadata (9.7 kB)\n", "Requirement already satisfied: anyio<5.0,>=3.0 in /usr/local/lib/python3.10/dist-packages (from gradio) (3.7.1)\n", "Collecting fastapi (from gradio)\n", " Downloading fastapi-0.112.1-py3-none-any.whl.metadata (27 kB)\n", "Collecting ffmpy (from gradio)\n", " Downloading 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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": [ "" ], "text/html": [ "
" ] }, "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" ] } ] }