Upload Untitled2.ipynb

Untitled2.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "2a0f61a3",
    "metadata": {},
    "outputs": [
@@ -10,12 +10,43 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy: 0.8417508417508418\n"
+      "Accuracy: 0.8417508417508418\n",
+      " * Serving Flask app \"__main__\" (lazy loading)\n",
+      " * Environment: production\n",
+      "\u001b[31m   WARNING: This is a development server. Do not use it in a production deployment.\u001b[0m\n",
+      "\u001b[2m   Use a production WSGI server instead.\u001b[0m\n",
+      " * Debug mode: on\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " * Restarting with watchdog (windowsapi)\n"
+     ]
+    },
+    {
+     "ename": "SystemExit",
+     "evalue": "1",
+     "output_type": "error",
+     "traceback": [
+      "An exception has occurred, use %tb to see the full traceback.\n",
+      "\u001b[1;31mSystemExit\u001b[0m\u001b[1;31m:\u001b[0m 1\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\91958\\anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3377: UserWarning: To exit: use 'exit', 'quit', or Ctrl-D.\n",
+      "  warn(\"To exit: use 'exit', 'quit', or Ctrl-D.\", stacklevel=1)\n"
      ]
     }
    ],
    "source": [
     "import pandas as pd\n",
+    "from flask import Flask, request, jsonify\n",
+    "\n",
     "from sklearn.compose import ColumnTransformer\n",
     "from sklearn.ensemble import RandomForestClassifier\n",
     "from sklearn.impute import SimpleImputer\n",
@@ -60,7 +91,30 @@
     "\n",
     "# Evaluate the model\n",
     "accuracy = pipeline.score(X_test, y_test)\n",
-    "print('Accuracy:', accuracy)\n"
+    "print('Accuracy:', accuracy)\n",
+    "\n",
+    "# Create Flask app\n",
+    "app = Flask(__name__)\n",
+    "\n",
+    "# Define API route for making predictions\n",
+    "@app.route('/predict', methods=['POST'])\n",
+    "def predict():\n",
+    "    # Get input data from request\n",
+    "    data = request.get_json()\n",
+    "\n",
+    "    # Convert input data to dataframe\n",
+    "    input_data = pd.DataFrame(data, index=[0])\n",
+    "\n",
+    "    # Make predictions using the trained pipeline\n",
+    "    predictions = pipeline.predict(input_data)\n",
+    "\n",
+    "    # Prepare response\n",
+    "    response = {'prediction': predictions[0]}\n",
+    "    return jsonify(response)\n",
+    "\n",
+    "# Run the Flask app\n",
+    "if __name__ == '__main__':\n",
+    "    app.run(debug=True)\n"
    ]
   },
   {