adding training code

iris_classifier.ipynb

@@ -0,0 +1,101 @@
+{
+  "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": 23,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Ee3EGX_zl1Ei",
+        "outputId": "13d12370-40e9-4fd6-a77c-6fc3721d2727"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Model saved as iris_knn.pkl\n"
+          ]
+        }
+      ],
+      "source": [
+        "from sklearn.datasets import load_iris\n",
+        "from sklearn.neighbors import KNeighborsClassifier\n",
+        "import joblib\n",
+        "\n",
+        "# Load dataset\n",
+        "iris = load_iris()\n",
+        "X = iris.data\n",
+        "y = iris.target\n",
+        "\n",
+        "# Train KNN\n",
+        "model = KNeighborsClassifier(n_neighbors=5)\n",
+        "model.fit(X, y)\n",
+        "\n",
+        "# Save model\n",
+        "joblib.dump((model, iris.target_names), \"iris_knn.pkl\")\n",
+        "\n",
+        "print(\"Model saved as iris_knn.pkl\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import requests\n",
+        "\n",
+        "url = \"https://tofighi-iris-detector-api.hf.space/predict\"\n",
+        "\n",
+        "data = {\n",
+        "    \"sepal_length\": 1.4,\n",
+        "    \"sepal_width\": 1.3,\n",
+        "    \"petal_length\": 2.4,\n",
+        "    \"petal_width\": 1\n",
+        "}\n",
+        "\n",
+        "resp = requests.post(url, json=data)\n",
+        "print(resp.json())"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "wMe3AXJVt70u",
+        "outputId": "9c01d308-679a-461f-f535-ef98851f944d"
+      },
+      "execution_count": 22,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "{'predicted_class': 'setosa', 'input': {'sepal_length': 1.4, 'sepal_width': 1.3, 'petal_length': 2.4, 'petal_width': 1.0}, 'class_probabilities': {'setosa': 0.8, 'versicolor': 0.2, 'virginica': 0.0}}\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "yYAcMc_Zyqpa"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}