20ad017_Mnist_model_using_SVC_and_RF.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "20ad017_Mnist model using SVC and RF.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 111,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KEKQDWsRwNMJ",
        "outputId": "8eb887c3-0d6e-4861-a571-303377f53fb4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ],
      "source": [
        "!pip install gradio\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "installing gradio"
      ],
      "metadata": {
        "id": "H88C-V3lwb3t"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import gradio as gr"
      ],
      "metadata": {
        "id": "ZQOHkEm5wREJ"
      },
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "importing gradio library . Gradio allows us to create interface for machine learning and deep learning model"
      ],
      "metadata": {
        "id": "v4jFUIcaw-KO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import fetch_openml\n",
        "mnist = fetch_openml('mnist_784')"
      ],
      "metadata": {
        "id": "FK_EEaOlwk2U"
      },
      "execution_count": 113,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "importing mnist dataset from scikit learn datasets"
      ],
      "metadata": {
        "id": "K_3NLihkxD0k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "a,b = mnist[\"data\"], mnist['target']"
      ],
      "metadata": {
        "id": "GP1QDHc3xQIy"
      },
      "execution_count": 114,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "in mnist dataset there are 2 columns (data and target).Assigning the data and target to the varaibles a and b"
      ],
      "metadata": {
        "id": "ab4L4dpgxhXp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "a.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h7WDKrwnxzKu",
        "outputId": "17870e9a-ed38-4c3e-f660-3ce1276d9ab6"
      },
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(70000, 784)"
            ]
          },
          "metadata": {},
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "b.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HfiIsHlUx-YE",
        "outputId": "64b2fe8e-eac9-45c5-f9ee-1ac9d2731ac4"
      },
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(70000,)"
            ]
          },
          "metadata": {},
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "getting the shape of a and b"
      ],
      "metadata": {
        "id": "CYGdfZvdyA-h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import matplotlib\n"
      ],
      "metadata": {
        "id": "R2BrPZivyEYy"
      },
      "execution_count": 117,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%matplotlib inline"
      ],
      "metadata": {
        "id": "kZIXYg0G6Rwb"
      },
      "execution_count": 118,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "importing the library's matplotlib for data visuvalization and numpy for computations"
      ],
      "metadata": {
        "id": "HB_EQXVGyYUY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "digit = np.array(a.iloc[25001])\n",
        "digit_image = digit.reshape(28,28)"
      ],
      "metadata": {
        "id": "OTjG648PyoXW"
      },
      "execution_count": 119,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "reshaping the image 25001 in the dataset"
      ],
      "metadata": {
        "id": "A3vvmp9xzZSQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(digit_image,cmap=matplotlib.cm.binary,interpolation=\"nearest\")\n",
        "plt.axis(\"off\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "vkmXSWxwzfDw",
        "outputId": "51f87bc9-18ca-44da-fcca-4e711aac78bd"
      },
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-0.5, 27.5, 27.5, -0.5)"
            ]
          },
          "metadata": {},
          "execution_count": 120
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "visualizing the image 25001"
      ],
      "metadata": {
        "id": "8dWKmbfN04pt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "b.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EzH-SPhg7zJQ",
        "outputId": "1f3990c7-741b-4137-9f9b-0a2268a3cf31"
      },
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(70000,)"
            ]
          },
          "metadata": {},
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "b[25001]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "6v6lRuLb0jme",
        "outputId": "ed41235a-8b97-4da0-b2df-ff7e9dc37510"
      },
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'4'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "checking the value of 25001"
      ],
      "metadata": {
        "id": "Wwm4QRu-1UuX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "a_train, a_test = a[:60000], a[60000:70000]"
      ],
      "metadata": {
        "id": "oEwv4gvB1dxy"
      },
      "execution_count": 123,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "splitting the data's of \"a\" into training set and testing set"
      ],
      "metadata": {
        "id": "EXNK24Ek1p-J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "b_train, b_test = b[:60000], b[60000:70000]"
      ],
      "metadata": {
        "id": "RdTK8Epn1zjG"
      },
      "execution_count": 124,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "splitting the data's of \"b\" into training set and testing set"
      ],
      "metadata": {
        "id": "74AFMBJL19Pn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "shuffle_index=np.random.permutation(60000)\n",
        "a_train= a_train.iloc[shuffle_index]\n",
        "b_train= b_train.iloc[shuffle_index]"
      ],
      "metadata": {
        "id": "NurD87zG2Qyi"
      },
      "execution_count": 127,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "randomly arranging the training data's for better accuracy"
      ],
      "metadata": {
        "id": "EZupw2K53hvt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "b_train = b_train.astype(np.int8)\n",
        "b_test = b_test.astype(np.int8)\n",
        "b_train_4 = (b_train == 4)\n",
        "b_test_4 = (b_test == 4)"
      ],
      "metadata": {
        "id": "lJtJCiEM3k-2"
      },
      "execution_count": 129,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "creating a 4 detector"
      ],
      "metadata": {
        "id": "L-ZauTIR_VXu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "b_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JUYXJ9N04SGZ",
        "outputId": "ebbd8ee4-4c2c-4203-e4b3-de85915b6321"
      },
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            ]
          },
          "metadata": {},
          "execution_count": 131
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression"
      ],
      "metadata": {
        "id": "plz6xnQ35OdP"
      },
      "execution_count": 132,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "importing logistic regression form sklearn"
      ],
      "metadata": {
        "id": "E1tBkWn4_fsC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "clf = LogisticRegression(tol = 0.1)"
      ],
      "metadata": {
        "id": "ojMSv_Vy8VIN"
      },
      "execution_count": 133,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clf.fit(a_train,b_train_4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bMGsbfiT8YF2",
        "outputId": "3002c7d8-a52b-49b0-b882-ab6e7c67c121"
      },
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(tol=0.1)"
            ]
          },
          "metadata": {},
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "fitting the training set into logistic regression"
      ],
      "metadata": {
        "id": "II0IgQGR_xEj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "clf.predict([digit])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DDuYIf2e8xvk",
        "outputId": "094ed871-dd37-4436-d72e-8deca69a74d2"
      },
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:451: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
            "  \"X does not have valid feature names, but\"\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ True])"
            ]
          },
          "metadata": {},
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "predicting the digits"
      ],
      "metadata": {
        "id": "Xe1Amjsd_4fN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "h = cross_val_score(clf, a_train , b_train_4, cv = 3, scoring=\"accuracy\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xJ6Ge-6h82Aj",
        "outputId": "8f724708-465c-4dfc-e535-db5a2f519feb"
      },
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "cross validating the logistic regression model.Using the logistic regression model for cross validation"
      ],
      "metadata": {
        "id": "oA86bUcb_-wV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "h"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kmfGFYe789rR",
        "outputId": "adf23995-d80f-45d4-fca9-f40ad248a822"
      },
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.9823 , 0.98025, 0.983  ])"
            ]
          },
          "metadata": {},
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "h.mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3Oaah05ZAXOl",
        "outputId": "244b6f60-a0fb-4a39-d164-50dfc21d4ed7"
      },
      "execution_count": 141,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.98185"
            ]
          },
          "metadata": {},
          "execution_count": 141
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "classification using svc"
      ],
      "metadata": {
        "id": "JjHkclzsAh40"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        "classifier2 = SVC()\n",
        "import numpy as np\n"
      ],
      "metadata": {
        "id": "zs_4m-ztGvwF"
      },
      "execution_count": 163,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "a_test=a_test.to_numpy()"
      ],
      "metadata": {
        "id": "JCbXGDOjJk3I"
      },
      "execution_count": 166,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "importing svc from sklearn.svm"
      ],
      "metadata": {
        "id": "CDv_EPxDAreJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "classifier2 = classifier2.fit(a.reshape(a.shape[0], 28*28), b)\n",
        "b_pred2 = classifier2.predict(a_test.reshape(a_test.shape[0],28*28))"
      ],
      "metadata": {
        "id": "9cUgeag6AvoO"
      },
      "execution_count": 167,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "reshaping and the predictions had been done"
      ],
      "metadata": {
        "id": "JIVkP3l6RapL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n"
      ],
      "metadata": {
        "id": "QF5c2wt6NCo0"
      },
      "execution_count": 175,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "importing the calssification_report for getting the classification report of the model"
      ],
      "metadata": {
        "id": "2QKRzE6TSJ47"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "b_pred2 = b_pred2.astype(np.int32)\n",
        "print(classification_report(b_test, b_pred2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1Q0JWBB1BGcp",
        "outputId": "db9a080e-d36f-4086-9813-18b321bf3597"
      },
      "execution_count": 176,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.99      0.99       980\n",
            "           1       0.99      1.00      1.00      1135\n",
            "           2       0.99      0.99      0.99      1032\n",
            "           3       0.99      0.99      0.99      1010\n",
            "           4       0.99      0.99      0.99       982\n",
            "           5       0.99      0.99      0.99       892\n",
            "           6       1.00      0.99      0.99       958\n",
            "           7       0.99      0.99      0.99      1028\n",
            "           8       0.99      0.99      0.99       974\n",
            "           9       0.99      0.98      0.98      1009\n",
            "\n",
            "    accuracy                           0.99     10000\n",
            "   macro avg       0.99      0.99      0.99     10000\n",
            "weighted avg       0.99      0.99      0.99     10000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "classification report of the prediction"
      ],
      "metadata": {
        "id": "BRxx3q0PL8ym"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "confusion_matrix(b_test, b_pred2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fPLNB39eNcZk",
        "outputId": "8e3193e6-69bc-4baf-a981-2702465eecf3"
      },
      "execution_count": 177,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 975,    0,    1,    0,    0,    1,    0,    1,    2,    0],\n",
              "       [   0, 1133,    0,    0,    0,    1,    0,    0,    1,    0],\n",
              "       [   1,    0, 1024,    0,    1,    0,    0,    4,    2,    0],\n",
              "       [   0,    0,    0, 1002,    0,    2,    0,    3,    3,    0],\n",
              "       [   0,    0,    0,    0,  975,    0,    1,    0,    0,    6],\n",
              "       [   2,    0,    0,    2,    0,  885,    2,    0,    0,    1],\n",
              "       [   2,    2,    0,    0,    2,    2,  949,    0,    1,    0],\n",
              "       [   0,    4,    4,    1,    0,    0,    0, 1015,    0,    4],\n",
              "       [   0,    0,    2,    2,    1,    2,    1,    1,  965,    0],\n",
              "       [   1,    3,    0,    4,    8,    2,    0,    3,    0,  988]])"
            ]
          },
          "metadata": {},
          "execution_count": 177
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "importing the cofusion matrix for the performance metrices"
      ],
      "metadata": {
        "id": "bb6_pNDCStSb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def predict_image(img):\n",
        "  img_3d=img.reshape(-1,28,28)\n",
        "  im_resize=img_3d/255.0\n",
        "  prediction=b_pred2(im_resize)\n",
        "  pred=np.argmax(prediction)\n",
        "  return pred"
      ],
      "metadata": {
        "id": "cDhUB1HSOfjU"
      },
      "execution_count": 193,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "reshaping and resizing the input image.Then predicting the input image."
      ],
      "metadata": {
        "id": "Tr6nbo4LTQTc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "iface = gr.Interface(predict_image, inputs=\"sketchpad\", outputs=\"label\")"
      ],
      "metadata": {
        "id": "f6bK11YNREhM"
      },
      "execution_count": 194,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "integrating the model with the interface"
      ],
      "metadata": {
        "id": "S7gk481rTTZJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "iface.launch(debug='True')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 660
        },
        "id": "3DmQ5VdBRIRo",
        "outputId": "fbf4436a-e4f6-4932-b747-4b2e33235683"
      },
      "execution_count": 195,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Colab notebook detected. This cell will run indefinitely so that you can see errors and logs. To turn off, set debug=False in launch().\n",
            "Running on public URL: https://28897.gradio.app\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting, check out Spaces (https://huggingface.co/spaces)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.lib.display.IFrame at 0x7f73f5fbfd10>"
            ],
            "text/html": [
              "\n",
              "        <iframe\n",
              "            width=\"900\"\n",
              "            height=\"500\"\n",
              "            src=\"https://28897.gradio.app\"\n",
              "            frameborder=\"0\"\n",
              "            allowfullscreen\n",
              "        ></iframe>\n",
              "        "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Keyboard interruption in main thread... closing server.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<fastapi.applications.FastAPI at 0x7f740fb16410>,\n",
              " 'http://127.0.0.1:7860/',\n",
              " 'https://28897.gradio.app')"
            ]
          },
          "metadata": {},
          "execution_count": 195
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Launching the SVC model"
      ],
      "metadata": {
        "id": "8MnOSha5Ta4b"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "using random forest"
      ],
      "metadata": {
        "id": "z6-kLZVuOf7R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier"
      ],
      "metadata": {
        "id": "m22VVcsiOn1u"
      },
      "execution_count": 183,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "classifi= RandomForestClassifier()"
      ],
      "metadata": {
        "id": "lSn5Q9uhPRxR"
      },
      "execution_count": 184,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "importing the random forest from sklearn"
      ],
      "metadata": {
        "id": "_-6rzBMvT-BW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "classifi.fit(np.array(a_train).reshape(a_train.shape[0], 28*28), b_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bPz7WQ6NPX2o",
        "outputId": "7f3e5dfd-9392-41db-9184-90af1b4a094f"
      },
      "execution_count": 185,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 185
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "fitting and classifing the data's"
      ],
      "metadata": {
        "id": "B9ESVfmrUGPc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "score=classifi.score(a_test, b_test)\n",
        "print('Random Tree Classifier:\\n' )\n",
        "\n",
        "print('Score\\t'+str(score))\n",
        "print('Accuracy:',(score*100))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "etEkeMeWQJ04",
        "outputId": "1eccab9c-cf3b-4770-e967-b7bea018acc2"
      },
      "execution_count": 186,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random Tree Classifier:\n",
            "\n",
            "Score\t0.9684\n",
            "Accuracy: 96.84\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "getting accuracy of the of the random classifier model"
      ],
      "metadata": {
        "id": "eQV0W4r3UPP4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def predict_image(img):\n",
        "  img_3d=img.reshape(-1,28,28)\n",
        "  im_resize=img_3d/255.0\n",
        "  prediction=classifi.fit(im_resize)\n",
        "  pred=np.argmax(prediction)\n",
        "  return pred"
      ],
      "metadata": {
        "id": "3ucTIqaxNe1p"
      },
      "execution_count": 187,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "reshaping and resizing the input image.Then predicting the input image."
      ],
      "metadata": {
        "id": "x62D7wc0Un8x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "iface = gr.Interface(predict_image, inputs=\"sketchpad\", outputs=\"label\")\n"
      ],
      "metadata": {
        "id": "_IHvLjwZNywj"
      },
      "execution_count": 188,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "integrating the model with the interface"
      ],
      "metadata": {
        "id": "yuXF30SmUptC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "iface.launch(debug='True')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 660
        },
        "id": "37FdbaMtN33f",
        "outputId": "99f3b6a4-8aa8-4e1e-a7a0-47e04e110696"
      },
      "execution_count": 190,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Colab notebook detected. This cell will run indefinitely so that you can see errors and logs. To turn off, set debug=False in launch().\n",
            "Running on public URL: https://39628.gradio.app\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting, check out Spaces (https://huggingface.co/spaces)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.lib.display.IFrame at 0x7f73f5220850>"
            ],
            "text/html": [
              "\n",
              "        <iframe\n",
              "            width=\"900\"\n",
              "            height=\"500\"\n",
              "            src=\"https://39628.gradio.app\"\n",
              "            frameborder=\"0\"\n",
              "            allowfullscreen\n",
              "        ></iframe>\n",
              "        "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Keyboard interruption in main thread... closing server.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<fastapi.applications.FastAPI at 0x7f740fb16410>,\n",
              " 'http://127.0.0.1:7860/',\n",
              " 'https://39628.gradio.app')"
            ]
          },
          "metadata": {},
          "execution_count": 190
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Launching the Random forest model"
      ],
      "metadata": {
        "id": "rHRvweAnUrmr"
      }
    }
  ]
}