sms_spam_with_view.ipynb

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      "metadata": {
        "id": "view-in-github",
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        "<a href=\"https://colab.research.google.com/github/Bluseass/sms-with-view-/blob/main/sms_spam_with_view.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "nltk.download() # d // all // q"
      ],
      "metadata": {
        "id": "KgZBUdMg43MU"
      },
      "execution_count": null,
      "outputs": []
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          "text": [
            "[[952   3]\n",
            " [ 21 139]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         ham       0.98      1.00      0.99       955\n",
            "        spam       0.98      0.87      0.92       160\n",
            "\n",
            "    accuracy                           0.98      1115\n",
            "   macro avg       0.98      0.93      0.95      1115\n",
            "weighted avg       0.98      0.98      0.98      1115\n",
            "\n",
            "0.97847533632287\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import re\n",
        "import string\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
        "\n",
        "\n",
        "data = pd.read_csv(\"/content/drive/MyDrive/nlp/SMSSpamCollection\", sep='\\t')\n",
        "data.columns = ['label', 'body_text']\n",
        "\n",
        "stopwords = nltk.corpus.stopwords.words('english')\n",
        "ps = nltk.PorterStemmer()\n",
        "\n",
        "def clean_text(text):\n",
        "    text = ''.join([word.lower() for word in text if word not in string.punctuation])\n",
        "    tokens = re.split('\\W+', text)\n",
        "    text = ' '.join([ps.stem(word) for word in tokens if word not in stopwords])\n",
        "    return text\n",
        "\n",
        "data['clean_text'] = data['body_text'].apply(lambda x: clean_text(x))\n",
        "\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(data['clean_text'], data['label'], test_size=0.2, random_state=42)\n",
        "\n",
        "\n",
        "tfidf_vect = TfidfVectorizer(analyzer=clean_text)\n",
        "X_train_tfidf = tfidf_vect.fit_transform(X_train)\n",
        "X_test_tfidf = tfidf_vect.transform(X_test)\n",
        "\n",
        "\n",
        "svm_model = SVC(kernel='linear')\n",
        "svm_model.fit(X_train_tfidf, y_train)\n",
        "\n",
        "\n",
        "predictions = svm_model.predict(X_test_tfidf)\n",
        "\n",
        "\n",
        "print(confusion_matrix(y_test,predictions))\n",
        "print(classification_report(y_test,predictions))\n",
        "print(accuracy_score(y_test, predictions))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "ВЬЮШКА\n"
      ],
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        "id": "pwaPK0UW4c_u"
      }
    },
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        "import ipywidgets as widgets\n",
        "def analyze_text(text):\n",
        "    text_tfidf = tfidf_vect.transform([text])\n",
        "    prediction = svm_model.predict(text_tfidf)\n",
        "    return f\"Результат анализа: {prediction[0]}\"\n",
        "\n",
        "# Функция обработки нажатия кнопки анализировать\n",
        "def analyze_button_clicked(b):\n",
        "    result = analyze_text(input_text.value)\n",
        "    output_text.value = result\n",
        "\n",
        "# Функция обработки нажатия кнопки очистить\n",
        "def clear_button_clicked(b):\n",
        "    input_text.value = ''\n",
        "    output_text.value = ''\n",
        "\n",
        "# Создание текстового поля для ввода текста\n",
        "input_text = widgets.Textarea(description='Текст:', layout={'width': '400px', 'height': '200px'})\n",
        "\n",
        "# Создание кнопки для запуска анализа текста\n",
        "analyze_button = widgets.Button(description='Анализировать')\n",
        "analyze_button.on_click(analyze_button_clicked)\n",
        "\n",
        "# Создание кнопки для очистки поля ввода\n",
        "clear_button = widgets.Button(description='Очистить')\n",
        "clear_button.on_click(clear_button_clicked)\n",
        "\n",
        "# Создание текстового поля для вывода результата анализа\n",
        "output_text = widgets.Textarea(description='Результат:', layout={'width': '400px', 'height': '50px'})\n",
        "\n",
        "# Отображение виджетов\n",
        "display(input_text)\n",
        "display(widgets.HBox([analyze_button, clear_button]))\n",
        "display(output_text)"
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