decisionscience/trial1

1{2 "cells": [3  {4   "cell_type": "code",5   "execution_count": null,6   "id": "728431f5",7   "metadata": {},8   "outputs": [],9   "source": []10  },11  {12   "cell_type": "code",13   "execution_count": 1,14   "id": "fd56baf1",15   "metadata": {},16   "outputs": [17    {18     "name": "stderr",19     "output_type": "stream",20     "text": [21      "2023-12-25 15:31:55.354 \n",22      "  \u001b[33m\u001b[1mWarning:\u001b[0m to view this Streamlit app on a browser, run it with the following\n",23      "  command:\n",24      "\n",25      "    streamlit run C:\\Users\\user\\anaconda3\\Lib\\site-packages\\ipykernel_launcher.py [ARGUMENTS]\n"26     ]27    }28   ],29   "source": [30    "import streamlit as st\n",31    "import pandas as pd\n",32    "import joblib\n",33    "\n",34    "# Load trained model\n",35    "model = joblib.load('mpg_model.pkl')  # Ensure this path is correct\n",36    "\n",37    "def user_input_features():\n",38    "    cylinders = st.sidebar.slider('Cylinders', 3, 8, 4)\n",39    "    displacement = st.sidebar.number_input('Displacement')\n",40    "    horsepower = st.sidebar.number_input('Horsepower')\n",41    "    weight = st.sidebar.number_input('Weight')\n",42    "    acceleration = st.sidebar.number_input('Acceleration')\n",43    "    model_year = st.sidebar.slider('Model Year', 70, 82, 76)\n",44    "    data = {'cylinders': cylinders,\n",45    "            'displacement': displacement,\n",46    "            'horsepower': horsepower,\n",47    "            'weight': weight,\n",48    "            'acceleration': acceleration,\n",49    "            'model_year': model_year}\n",50    "    features = pd.DataFrame(data, index=[0])\n",51    "    return features\n",52    "\n",53    "# Main Streamlit app interface\n",54    "st.write(\"\"\"\n",55    "# Simple MPG Prediction App\n",56    "This app predicts the **Miles Per Gallon (MPG)** of your car!\n",57    "\"\"\")\n",58    "\n",59    "# User input features\n",60    "input_df = user_input_features()\n",61    "\n",62    "# Display the user input features\n",63    "st.subheader('User Input features')\n",64    "st.write(input_df)\n",65    "\n",66    "# Predict and display the output\n",67    "st.subheader('Prediction')\n",68    "prediction = model.predict(input_df)\n",69    "st.write(f'Predicted MPG: {prediction[0]:.2f}')"70   ]71  },72  {73   "cell_type": "code",74   "execution_count": null,75   "id": "f8836f1f",76   "metadata": {},77   "outputs": [],78   "source": []79  }80 ],81 "metadata": {82  "kernelspec": {83   "display_name": "Python 3 (ipykernel)",84   "language": "python",85   "name": "python3"86  },87  "language_info": {88   "codemirror_mode": {89    "name": "ipython",90    "version": 391   },92   "file_extension": ".py",93   "mimetype": "text/x-python",94   "name": "python",95   "nbconvert_exporter": "python",96   "pygments_lexer": "ipython3",97   "version": "3.11.5"98  }99 },100 "nbformat": 4,101 "nbformat_minor": 5102}103