{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OrxTxENAIO_g"
      },
      "source": [
        "# Spam Detection Project README\n",
        "\n",
        "Spam Detection project, for users to classify messages as spam or not.\n",
        "\n",
        "## Table of Contents\n",
        "\n",
        "- [Data Collection and Preprocessing](#data-collection-and-preprocessing)\n",
        "  - [Mount Google Drive](#mount-google-drive)\n",
        "  - [Install Required Libraries](#install-required-libraries)\n",
        "  - [Load and Prepare Data](#load-and-prepare-data)\n",
        "  - [Prepare Data Labels](#prepare-data-labels)\n",
        "  - [Split Data](#split-data)\n",
        "- [Model Building and Training](#model-building-and-training)\n",
        "  - [Initialize Tokenizer](#initialize-tokenizer)\n",
        "  - [Tokenize Data](#tokenize-data)\n",
        "  - [Create TensorFlow Datasets](#create-tensorflow-datasets)\n",
        "  - [Define Training Arguments](#define-training-arguments)\n",
        "  - [Initialize and Train Model](#initialize-and-train-model)\n",
        "- [Model Evaluation and Inference](#model-evaluation-and-inference)\n",
        "  - [Evaluate Model](#evaluate-model)\n",
        "  - [Generate Predictions](#generate-predictions)\n",
        "  - [Save Trained Model](#save-trained-model)\n",
        "- [Interactive Gradio Interface](#interactive-gradio-interface)\n",
        "  - [Inference on Sample Text](#inference-on-sample-text)\n",
        "  - [Create Gradio Interface](#create-gradio-interface)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c_5oaNFSI1oB"
      },
      "source": [
        "## Data Collection and Preprocessing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UAerPRTUCyny"
      },
      "source": [
        "### Mount Google Drive\n",
        "Mounting Google Drive in Google Colab to access files and data stored in Google Drive within the notebook."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "dEGvOVWFsQcf"
      },
      "outputs": [],
      "source": [
        "# from google.colab import drive\n",
        "# drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QFpdIglgDBZO"
      },
      "source": [
        "### Install Required Libraries\n",
        "Installing the necessary libraries datasets, transformers, and gradio using the pip package manager."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gzn-RVdH1LGD",
        "outputId": "cbca1211-e2ae-4175-b8bb-062419955eff"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "Building wheels for collected packages: ffmpy\n",
            "  Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "Installing collected packages: tokenizers, safetensors, pydub, ffmpy, xxhash, websockets, semantic-version, python-multipart, orjson, markdown-it-py, h11, dill, aiofiles, uvicorn, starlette, multiprocess, mdit-py-plugins, huggingface-hub, httpcore, transformers, httpx, fastapi, gradio-client, datasets, gradio\n",
            "  Attempting uninstall: markdown-it-py\n",
            "    Found existing installation: markdown-it-py 3.0.0\n",
            "    Uninstalling markdown-it-py-3.0.0:\n",
            "      Successfully uninstalled markdown-it-py-3.0.0\n",
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            "    Found existing installation: mdit-py-plugins 0.4.0\n",
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            "      Successfully uninstalled mdit-py-plugins-0.4.0\n",
            "Successfully installed aiofiles-23.2.1 datasets-2.14.4 dill-0.3.7 fastapi-0.101.0 ffmpy-0.3.1 gradio-3.40.1 gradio-client-0.4.0 h11-0.14.0 httpcore-0.17.3 httpx-0.24.1 huggingface-hub-0.16.4 markdown-it-py-2.2.0 mdit-py-plugins-0.3.3 multiprocess-0.70.15 orjson-3.9.4 pydub-0.25.1 python-multipart-0.0.6 safetensors-0.3.2 semantic-version-2.10.0 starlette-0.27.0 tokenizers-0.13.3 transformers-4.31.0 uvicorn-0.23.2 websockets-11.0.3 xxhash-3.3.0\n"
          ]
        }
      ],
      "source": [
        "! pip install datasets transformers gradio"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YK0DqnKcDLMZ"
      },
      "source": [
        "### Load and Prepare Data\n",
        "Reading and preparing the dataset from different sources, including Kaggle, Hugging Face dataset, and CSV files. It concatenates all dataframes to create a single DataFrame containing spam detection data.\n"
      ]
    },
    {
      "cell_type": "code",
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        "id": "awPXefiYqQsF",
        "outputId": "ec530e3f-c076-4901-f01e-3445a0fa9549"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4347b72765dc4b648ceb62e9070f658d",
              "version_major": 2,
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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              "model_id": "052295558c1643dda2b00a8a01e283ce",
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              "model_id": "39ff21b018a34a0b9a797d79ff2956fe",
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          "metadata": {},
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              "model_id": "6b4bf1cf451d461dabdb15731186d737",
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        {
          "data": {
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              "model_id": "cc8c9effe14647d1a479038c3072857e",
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "not_spam    15625\n",
            "spam        11747\n",
            "Name: label, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "from datasets import load_dataset\n",
        "\n",
        "drive_data_path = \"/content/\"\n",
        "\n",
        "def read_data():\n",
        "    # Read Kaggle data\n",
        "    df_kaggle = pd.read_csv(drive_data_path + 'kaggle.txt', sep='\\t', names=[\"label\", \"message\"])\n",
        "    df_kaggle.loc[df_kaggle['label'] == 'ham', 'label'] = 'not_spam'\n",
        "\n",
        "    # Load data from Hugging Face dataset\n",
        "    data_ = load_dataset(\"Deysi/spam-detection-dataset\")\n",
        "    texts_train = [item['text'] for item in data_[\"train\"]]\n",
        "    labels_train = [item['label'] for item in data_[\"train\"]]\n",
        "    df_hugging_face_train = pd.DataFrame({'label': labels_train, 'message': texts_train})\n",
        "    texts_test = [item['text'] for item in data_[\"test\"]]\n",
        "    labels_test = [item['label'] for item in data_[\"test\"]]\n",
        "    df_hugging_face_test = pd.DataFrame({'label': labels_test, 'message': texts_test})\n",
        "\n",
        "    # Concatenate Hugging Face dataset train and test data\n",
        "    df_hugging_face = pd.concat([df_hugging_face_train, df_hugging_face_test], ignore_index=True)\n",
        "\n",
        "    # Read CSV file data\n",
        "    df_csv_train = pd.read_csv(drive_data_path + \"train.csv\")\n",
        "    df_csv_train = df_csv_train[['label', 'text']]\n",
        "    df_csv_train = df_csv_train.rename(columns={'text': 'message'})\n",
        "    df_csv_test = pd.read_csv(drive_data_path + \"test.csv\")\n",
        "    df_csv_test = df_csv_test[['label', 'text']]\n",
        "    df_csv_test = df_csv_test.rename(columns={'text': 'message'})\n",
        "\n",
        "    # Concatenate all dataframes\n",
        "    df = pd.concat([df_kaggle, df_hugging_face, df_csv_train, df_csv_test], ignore_index=True)\n",
        "\n",
        "    return df\n",
        "\n",
        "df = read_data()\n",
        "\n",
        "# Display value counts of labels\n",
        "print(df['label'].value_counts())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SRMTXrclDVNX"
      },
      "source": [
        "### Prepare Data Labels\n",
        "Preparing the target labels for classification by converting them to binary values (0 or 1) using one-hot encoding."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "EsvEwG98qq2b"
      },
      "outputs": [],
      "source": [
        "X,y=list(df['message']),list(df['label'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "aZpBn3MtssZL"
      },
      "outputs": [],
      "source": [
        "y=list(pd.get_dummies(y,drop_first=True)['spam'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PRQHUraUDfQN"
      },
      "source": [
        "### Split Data\n",
        "Splitting the data into training and testing sets using train_test_split from scikit-learn."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dLFDWda0rIKw",
        "outputId": "fd45b292-deb9-493d-fbb8-2b8e9c27eef4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "X,y shape:  27372 27372\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "print(\"X,y shape: \",len(X),len(y))\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZvAONDqeL-BT"
      },
      "source": [
        "## Model Building and Training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TRS4Vm32Do1N"
      },
      "source": [
        "### Initialize Tokenizer\n",
        "Initializing the DistilBERT tokenizer from the Hugging Face library."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
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            "237dad4062014d6185ec00dfd26d6545"
          ]
        },
        "id": "bcNEJ6perOSs",
        "outputId": "1f8c4da5-c0fb-44f8-fd8c-c07b4058f25e"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ecf70d4a4dfc4d5e9781659bdb588f5a",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e3a121298df44bd2ad9bb75726bf25f7",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1738f7695d4442218e0a96f2e54087e0",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6cd7b346ec6e4414b16ea1bbd5256597",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/483 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from transformers import DistilBertTokenizerFast\n",
        "tokenizer = DistilBertTokenizerFast.from_pretrained('distilbert-base-uncased')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ogQsUAw-Dt-R"
      },
      "source": [
        "### Tokenize Data\n",
        "Tokenizing the training and testing data using the initialized tokenizer."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "-OL3fgLvrXvH"
      },
      "outputs": [],
      "source": [
        "train_encodings = tokenizer(X_train, truncation=True, padding=True)\n",
        "test_encodings = tokenizer(X_test, truncation=True, padding=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d9VAEWTcD1YJ"
      },
      "source": [
        "### Create TensorFlow Datasets\n",
        "Creating TensorFlow datasets using the tokenized encodings and labels."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "9B42CTCnrrEx"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "train_dataset = tf.data.Dataset.from_tensor_slices((\n",
        "    dict(train_encodings),\n",
        "    y_train\n",
        "))\n",
        "\n",
        "test_dataset = tf.data.Dataset.from_tensor_slices((\n",
        "    dict(test_encodings),\n",
        "    y_test\n",
        "))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mWu5rzb6D6ah"
      },
      "source": [
        "### Define Training Arguments\n",
        "Defining the training arguments for the TFTrainer from the Hugging Face library."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "NH1dupK0rzfn"
      },
      "outputs": [],
      "source": [
        "from transformers import TFDistilBertForSequenceClassification, TFTrainer, TFTrainingArguments\n",
        "\n",
        "# Define training arguments\n",
        "training_args = TFTrainingArguments(\n",
        "    output_dir='./results',                    # Directory to save model checkpoints and results\n",
        "    num_train_epochs=2,                         # Number of training epochs\n",
        "    per_device_train_batch_size=8,              # Batch size for training\n",
        "    per_device_eval_batch_size=16,              # Batch size for evaluation\n",
        "    warmup_steps=500,                           # Number of warmup steps for learning rate scheduling\n",
        "    weight_decay=0.01,                          # Weight decay for regularization\n",
        "    logging_dir='./logs',                       # Directory for storing logs\n",
        "    logging_steps=10,                           # Log every specified number of steps\n",
        "    evaluation_strategy=\"steps\",                # Evaluation strategy (\"steps\" or \"epoch\")\n",
        "    eval_steps=500,                             # Number of steps between evaluations\n",
        "    save_total_limit=1,                         # Limit the number of checkpoints saved\n",
        "    metric_for_best_model=\"eval_accuracy\",      # Metric for saving the best model checkpoint\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J68L0_JeD_L0"
      },
      "source": [
        "### Initialize and Train Model\n",
        "Initializes the DistilBERT model, initializes the TFTrainer, and trains the model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PZvTrEcfr7k-",
        "outputId": "7807a2d0-8f53-4ddd-bcd9-3a4b9726f82a"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of the PyTorch model were not used when initializing the TF 2.0 model TFDistilBertForSequenceClassification: ['vocab_layer_norm.bias', 'vocab_transform.bias', 'vocab_transform.weight', 'vocab_projector.bias', 'vocab_layer_norm.weight']\n",
            "- This IS expected if you are initializing TFDistilBertForSequenceClassification from a PyTorch model trained on another task or with another architecture (e.g. initializing a TFBertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing TFDistilBertForSequenceClassification from a PyTorch model that you expect to be exactly identical (e.g. initializing a TFBertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights or buffers of the TF 2.0 model TFDistilBertForSequenceClassification were not initialized from the PyTorch model and are newly initialized: ['pre_classifier.weight', 'pre_classifier.bias', 'classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "# Create the DistilBERT model within the strategy scope\n",
        "with training_args.strategy.scope():\n",
        "    model = TFDistilBertForSequenceClassification.from_pretrained(\"distilbert-base-uncased\")\n",
        "\n",
        "# Set the eval_steps value\n",
        "training_args.eval_steps = 500\n",
        "\n",
        "# Initialize the Trainer for training\n",
        "trainer = TFTrainer(\n",
        "    model=model,                         # the instantiated 🤗 Transformers model to be trained\n",
        "    args=training_args,                  # training arguments, defined above\n",
        "    train_dataset=train_dataset,         # training dataset\n",
        "    eval_dataset=test_dataset             # evaluation dataset\n",
        ")\n",
        "\n",
        "# Train the model\n",
        "trainer.train()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TgJ6_2YbMUBO"
      },
      "source": [
        "## Model Evaluation and Inference"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fUjJkKBxEYRr"
      },
      "source": [
        "### Evaluate Model\n",
        "Evaluating the trained model using the testing dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R534aDi3xD0s",
        "outputId": "7f7fd521-69b0-46a5-cb3d-84c91ad6ac75"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'eval_loss': 0.007443295970950113}"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trainer.evaluate(test_dataset)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fn336SjyHixq"
      },
      "source": [
        "### Save Trained Model\n",
        "Saving the trained model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "okD5we1NwhQW"
      },
      "outputs": [],
      "source": [
        "trainer.save_model('spam_detection_model')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WyPfTOKaMdem"
      },
      "source": [
        "## Interactive Gradio Interface"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MW9ioqk7HpkN"
      },
      "source": [
        "### Inference on Sample Text\n",
        "Performing inference on a sample text using the trained model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f9MbFGrEyNTS",
        "outputId": "d5b335c2-7dba-4a2a-c3f6-9976da72807f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Sample Message: Hi there, how's it going?\n",
            "Predicted Label: No need to worry, Not a spam message.\n"
          ]
        }
      ],
      "source": [
        "# Sample text you want to classify\n",
        "sample_text = \"Hi there, how's it going?\"\n",
        "\n",
        "# Preprocess the sample text using the tokenizer\n",
        "sample_encodings = tokenizer(sample_text, truncation=True, padding=True, return_tensors=\"tf\")\n",
        "\n",
        "# Perform inference\n",
        "with training_args.strategy.scope():\n",
        "    logits = model(sample_encodings.input_ids).logits\n",
        "\n",
        "# Convert logits to probabilities using softmax\n",
        "probabilities = tf.nn.softmax(logits, axis=-1)\n",
        "\n",
        "# Get the predicted class\n",
        "predicted_class = tf.argmax(probabilities, axis=-1).numpy()[0]\n",
        "\n",
        "# Map the predicted class to label\n",
        "label_mapping = {0: \"No need to worry, Not a spam message.\", 1: \"This message has been identified as spam.\"}\n",
        "predicted_label = label_mapping[predicted_class]\n",
        "\n",
        "print(\"Sample Message:\", sample_text)\n",
        "print(\"Predicted Label:\", predicted_label)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rcvb9ADvHuvx"
      },
      "source": [
        "### Create Gradio Interface\n",
        "Creating a Gradio interface for interactive spam detection using the trained model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 616
        },
        "id": "uk3gHaanqq2W",
        "outputId": "b06ff574-2fb3-4b01-bed7-148030c0d72d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
            "Note: opening Chrome Inspector may crash demo inside Colab notebooks.\n",
            "\n",
            "To create a public link, set `share=True` in `launch()`.\n"
          ]
        },
        {
          "data": {
            "application/javascript": "(async (port, path, width, height, cache, element) => {\n                        if (!google.colab.kernel.accessAllowed && !cache) {\n                            return;\n                        }\n                        element.appendChild(document.createTextNode(''));\n                        const url = await google.colab.kernel.proxyPort(port, {cache});\n\n                        const external_link = document.createElement('div');\n                        external_link.innerHTML = `\n                            <div style=\"font-family: monospace; margin-bottom: 0.5rem\">\n                                Running on <a href=${new URL(path, url).toString()} target=\"_blank\">\n                                    https://localhost:${port}${path}\n                                </a>\n                            </div>\n                        `;\n                        element.appendChild(external_link);\n\n                        const iframe = document.createElement('iframe');\n                        iframe.src = new URL(path, url).toString();\n                        iframe.height = height;\n                        iframe.allow = \"autoplay; camera; microphone; clipboard-read; clipboard-write;\"\n                        iframe.width = width;\n                        iframe.style.border = 0;\n                        element.appendChild(iframe);\n                    })(7862, \"/\", \"100%\", 500, false, window.element)",
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": []
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import gradio as gr\n",
        "import tensorflow as tf\n",
        "import random\n",
        "\n",
        "def process_input(text):\n",
        "    # Preprocess the sample text using the tokenizer\n",
        "    encodings = tokenizer(text, truncation=True, padding=True, return_tensors=\"tf\")\n",
        "\n",
        "    # Perform inference\n",
        "    logits = model(encodings.input_ids).logits\n",
        "\n",
        "    # Convert logits to probabilities using softmax\n",
        "    probabilities = tf.nn.softmax(logits, axis=-1)\n",
        "\n",
        "    # Get the predicted class\n",
        "    predicted_class = tf.argmax(probabilities, axis=-1).numpy()[0]\n",
        "\n",
        "    # Map the predicted class to label\n",
        "    label_mapping = {\n",
        "        0: '<b><div style=\"font-size:16px; text-align:center;\">No need to worry, Not a spam message.</div></b>',\n",
        "        1: '<b><div style=\"font-size:16px; color:#ff3b5c; text-align:center;\">Warning⚠️: This message has been identified as spam.</div></b>',\n",
        "    }\n",
        "    predicted_label = label_mapping[predicted_class]\n",
        "\n",
        "    return [\n",
        "        {\n",
        "            \"Spam\": float(probabilities.numpy()[0][1]),\n",
        "            \"Not a Spam\": float(probabilities.numpy()[0][0]),\n",
        "        },\n",
        "        predicted_label,\n",
        "    ]\n",
        "\n",
        "\n",
        "# Define the Gradio interface\n",
        "title = \"Spam Detector⚠️\"\n",
        "examples = [\n",
        "    \"Dear Customer, Your account has been compromised. Click the link below to verify your account details immediately or risk suspension. **(Example 1)**\",\n",
        "    \"You've been selected as the lucky winner of our international sweepstakes! To claim your prize, reply with your full name, address, and bank details. <font color='blue' style='background-color: lightgray;'>(Example 2)</font>\",\n",
        "    \"Congratulations! You've won a free iPhone X. Click the link to claim your prize.\",\n",
        "    \"URGENT: Your bank account has been compromised. Click here to reset your password.\",\n",
        "    \"Get rich quick! Invest in our exclusive program and earn thousands overnight.\",\n",
        "    \"Your prescription refill is ready for pickup at your local pharmacy. Visit us at your convenience\",\n",
        "    \"Reminder: Your monthly utility bill is due on August 20th. Please make the payment.\",\n",
        "    \"You've been selected as the lucky winner of a million-dollar lottery. Reply to claim.\",\n",
        "    \"Limited time offer: Double your money with our amazing investment opportunity.\",\n",
        "    \"Hi, just checking in to see how you're doing. Let's catch up soon.\",\n",
        "    \"Reminder: Your dentist appointment is scheduled for tomorrow at 2 PM.\",\n",
        "    \"Invitation: Join us for a webinar on digital marketing strategies. Register now!\",\n",
        "    \"Your application for the scholarship has been reviewed. We're pleased to inform you that you've been selected.\",\n",
        "    \"Hi there! Just wanted to check in and see how you're doing.\",\n",
        "    \"Reminder: Your friend's birthday is coming up. Don't forget to send them a message.\",\n",
        "    \"Thank you for your purchase. Your order has been successfully processed.\",\n",
        "    \"Your monthly newsletter is here! Stay updated with the latest news and updates.\",\n",
        "    \"Invitation: Join us for a community clean-up event this weekend. Let's make a difference together.\",\n",
        "    \"Reminder: Your scheduled appointment is tomorrow. We look forward to seeing you.\",\n",
        "    \"Good news! You've earned a reward for your loyalty. Check your account for details.\",\n",
        "    \"Your recent transaction has been approved. Please keep this email for your records.\",\n",
        "    \"Exciting announcement: Our new store location is now open. Visit us and receive a special discount.\",\n",
        "    \"Welcome to our online community! Here's how to get started and connect with others.\",\n",
        "    \"Your request has been received and is being processed. We'll update you with the status soon.\",\n",
        "    \"Upcoming event: Join us for a free cooking class this Saturday. Learn new recipes and techniques.\",\n",
        "    \"Reminder: Don't forget to vote in the upcoming election. Your voice matters.\",\n",
        "    \"Join our book club and dive into a world of fascinating stories. Here's how to join.\",\n",
        "]\n",
        "\n",
        "\n",
        "# Create Gradio components\n",
        "input_text = gr.Textbox(\n",
        "    lines=3, label=\"Enter the SMS/Message/Email you received\", autofocus=True\n",
        ")\n",
        "output_text = gr.HTML(\"\", label=\"Output\")\n",
        "probabilities_text = gr.Label(\"\", label=\"Probabilities\")\n",
        "\n",
        "random.shuffle(examples)\n",
        "\n",
        "# Initialize the Gradio interface\n",
        "model_gui = gr.Interface(\n",
        "    fn=process_input,\n",
        "    inputs=input_text,\n",
        "    outputs=[probabilities_text, output_text],\n",
        "    title=title,\n",
        "    examples=examples,\n",
        "    interpretation=\"default\",\n",
        "    theme=\"shivi/calm_seafoam\",\n",
        "    css=\"\"\"*{font-family:'IBM Plex Mono';}\"\"\",\n",
        "    examples_per_page=15,\n",
        ")\n",
        "\n",
        "# Launch the Gradio interface\n",
        "model_gui.launch()"
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