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gradio_huggingface.ipynb
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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"provenance": []
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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},
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"language_info": {
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"name": "python"
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}
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},
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"cells": [
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{
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"source": [
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"!pip install gradio"
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],
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"cell_type": "code",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "AG3LJPwN8_bz",
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"outputId": "579e2d88-15d8-4500-a6e8-ea10ec00fabf"
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},
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"execution_count": 2,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Collecting gradio\n",
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" Downloading gradio-4.41.0-py3-none-any.whl.metadata (15 kB)\n",
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"Collecting aiofiles<24.0,>=22.0 (from gradio)\n",
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"Collecting fastapi (from gradio)\n",
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" Downloading ffmpy-0.4.0-py3-none-any.whl.metadata (2.9 kB)\n",
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"Collecting gradio-client==1.3.0 (from gradio)\n",
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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",
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" Attempting uninstall: tomlkit\n",
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" Found existing installation: tomlkit 0.13.0\n",
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" Uninstalling tomlkit-0.13.0:\n",
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" Successfully uninstalled tomlkit-0.13.0\n",
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"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"
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}
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 646
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},
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"id": "sk8EestI8YRY",
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"outputId": "d0434b66-0088-4611-88eb-e031e246e264"
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},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"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",
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"\n",
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"Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
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+
"Running on public URL: https://8f53fe8496666cb51a.gradio.live\n",
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"\n",
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| 170 |
+
"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"
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]
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},
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{
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"output_type": "display_data",
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"data": {
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"text/plain": [
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"<IPython.core.display.HTML object>"
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],
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"text/html": [
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| 180 |
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"<div><iframe src=\"https://8f53fe8496666cb51a.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
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]
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},
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"metadata": {}
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+
},
|
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{
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"output_type": "execute_result",
|
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"data": {
|
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+
"text/plain": []
|
| 189 |
+
},
|
| 190 |
+
"metadata": {},
|
| 191 |
+
"execution_count": 3
|
| 192 |
+
}
|
| 193 |
+
],
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| 194 |
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"source": [
|
| 195 |
+
"import gradio as gr\n",
|
| 196 |
+
"import pandas as pd\n",
|
| 197 |
+
"import numpy as np\n",
|
| 198 |
+
"import pickle\n",
|
| 199 |
+
"\n",
|
| 200 |
+
"# Load the trained model from the pickle file\n",
|
| 201 |
+
"with open('best_arima_models.pkl', 'rb') as f:\n",
|
| 202 |
+
" model = pickle.load(f)\n",
|
| 203 |
+
"\n",
|
| 204 |
+
"def predict_demand(mapped_code, num_months):\n",
|
| 205 |
+
" try:\n",
|
| 206 |
+
" # Retrieve the specific model for the mapped code\n",
|
| 207 |
+
" if mapped_code not in model:\n",
|
| 208 |
+
" return f\"No model found for Mapped Code: {mapped_code}\"\n",
|
| 209 |
+
"\n",
|
| 210 |
+
" model_for_code = model[mapped_code]\n",
|
| 211 |
+
"\n",
|
| 212 |
+
" # Generate a date range for the prediction period\n",
|
| 213 |
+
" dates = pd.date_range(start=pd.Timestamp.today(), periods=num_months, freq='M')\n",
|
| 214 |
+
"\n",
|
| 215 |
+
" # Make predictions\n",
|
| 216 |
+
" future_steps = len(dates)\n",
|
| 217 |
+
" forecast = model_for_code.forecast(steps=future_steps)\n",
|
| 218 |
+
"\n",
|
| 219 |
+
" # Prepare a DataFrame for display\n",
|
| 220 |
+
" df = pd.DataFrame({\n",
|
| 221 |
+
" 'Date': dates.strftime('%Y-%m'),\n",
|
| 222 |
+
" 'Predicted Demand': forecast\n",
|
| 223 |
+
" })\n",
|
| 224 |
+
"\n",
|
| 225 |
+
" return df\n",
|
| 226 |
+
"\n",
|
| 227 |
+
" except Exception as e:\n",
|
| 228 |
+
" print(f\"Error occurred: {e}\")\n",
|
| 229 |
+
" return f\"An error occurred: {str(e)}\"\n",
|
| 230 |
+
"\n",
|
| 231 |
+
"# Gradio Interface Definition\n",
|
| 232 |
+
"gr.Interface(\n",
|
| 233 |
+
" fn=predict_demand,\n",
|
| 234 |
+
" inputs=[\n",
|
| 235 |
+
" gr.Textbox(label=\"Mapped Code\", placeholder=\"Enter mapped code here\"),\n",
|
| 236 |
+
" gr.Slider(minimum=1, maximum=12, step=1, label=\"Number of Months\")\n",
|
| 237 |
+
" ],\n",
|
| 238 |
+
" outputs=[\n",
|
| 239 |
+
" gr.Dataframe(label=\"Predicted Demand\")\n",
|
| 240 |
+
" ],\n",
|
| 241 |
+
" title=\"Demand Forecasting\",\n",
|
| 242 |
+
" description=\"Enter the mapped code and the number of months to predict future demand.\"\n",
|
| 243 |
+
").launch()\n"
|
| 244 |
+
]
|
| 245 |
+
}
|
| 246 |
+
]
|
| 247 |
+
}
|