{
 "cells": [
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   "cell_type": "code",
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    {
     "name": "stdout",
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     "text": [
      "Epoch 1/10\n",
      "\u001b[1m7964/7964\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m383s\u001b[0m 48ms/step - accuracy: 0.7637 - loss: 0.4815 - val_accuracy: 0.8195 - val_loss: 0.3929 - learning_rate: 0.0010\n",
      "Epoch 2/10\n",
      "\u001b[1m7964/7964\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m360s\u001b[0m 45ms/step - accuracy: 0.8561 - loss: 0.3267 - val_accuracy: 0.8256 - val_loss: 0.3854 - learning_rate: 0.0010\n",
      "Epoch 3/10\n",
      "\u001b[1m7964/7964\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m373s\u001b[0m 47ms/step - accuracy: 0.8937 - loss: 0.2503 - val_accuracy: 0.8250 - val_loss: 0.4444 - learning_rate: 0.0010\n",
      "Epoch 4/10\n",
      "\u001b[1m7964/7964\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m377s\u001b[0m 47ms/step - accuracy: 0.9269 - loss: 0.1794 - val_accuracy: 0.8173 - val_loss: 0.4580 - learning_rate: 0.0010\n",
      "Epoch 5/10\n",
      "\u001b[1m7964/7964\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m385s\u001b[0m 48ms/step - accuracy: 0.9496 - loss: 0.1284 - val_accuracy: 0.8147 - val_loss: 0.5704 - learning_rate: 0.0010\n",
      "\u001b[1m2213/2213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 9ms/step - accuracy: 0.8228 - loss: 0.3848\n",
      "Test Accuracy: 0.8214734792709351\n",
      "\u001b[1m2213/2213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 11ms/step\n",
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.90      0.87     46733\n",
      "           1       0.77      0.68      0.72     24052\n",
      "\n",
      "    accuracy                           0.82     70785\n",
      "   macro avg       0.81      0.79      0.79     70785\n",
      "weighted avg       0.82      0.82      0.82     70785\n",
      "\n",
      "\n",
      "Confusion Matrix:\n",
      "[[41892  4841]\n",
      " [ 7796 16256]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tensorflow.keras.preprocessing.text import Tokenizer\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Embedding, LSTM, Dense, Dropout\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "from tensorflow.keras.callbacks import ReduceLROnPlateau, TensorBoard, EarlyStopping\n",
    "\n",
    "# load data\n",
    "df = pd.read_csv('combined_data.csv')\n",
    "\n",
    "# Tokenize the text\n",
    "tokenizer = Tokenizer()\n",
    "tokenizer.fit_on_texts(df['title'])\n",
    "X = tokenizer.texts_to_sequences(df['title'])\n",
    "X = pad_sequences(X)\n",
    "\n",
    "# Encode the target variable\n",
    "encoder = LabelEncoder()\n",
    "y = encoder.fit_transform(df['source'])\n",
    "y = to_categorical(y)\n",
    "\n",
    "# Split the data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Build the LSTM model\n",
    "model = Sequential()\n",
    "model.add(Embedding(len(tokenizer.word_index) + 1, 128))\n",
    "model.add(LSTM(128, return_sequences=True))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(LSTM(64))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(len(encoder.classes_), activation='softmax'))\n",
    "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "# Learning rate scheduler\n",
    "lr_scheduler = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=3, min_lr=1e-5)\n",
    "\n",
    "# TensorBoard callback for logging\n",
    "tensorboard_callback = TensorBoard(log_dir='./logs', histogram_freq=1)\n",
    "\n",
    "# Early stopping to prevent overfitting\n",
    "early_stopping = EarlyStopping(monitor='val_loss', patience=3, restore_best_weights=True)\n",
    "\n",
    "# Train the model with callbacks\n",
    "model.fit(X_train, y_train, epochs=10, batch_size=32, validation_split=0.1, \n",
    "          callbacks=[lr_scheduler, tensorboard_callback, early_stopping])\n",
    "\n",
    "# Evaluate the model\n",
    "loss, accuracy = model.evaluate(X_test, y_test)\n",
    "print(f\"Test Accuracy: {accuracy}\")\n",
    "\n",
    "# Predictions and evaluation\n",
    "y_pred = model.predict(X_test)\n",
    "y_pred_classes = y_pred.argmax(axis=1)\n",
    "y_test_classes = y_test.argmax(axis=1)\n",
    "\n",
    "print(\"\\nClassification Report:\")\n",
    "print(classification_report(y_test_classes, y_pred_classes))\n",
    "\n",
    "print(\"\\nConfusion Matrix:\")\n",
    "print(confusion_matrix(y_test_classes, y_pred_classes))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
     ]
    }
   ],
   "source": [
    "# save model\n",
    "model.save('news_classifier.h5')\n",
    "\n",
    "# save tokenizer\n",
    "import pickle\n",
    "with open('tokenizer.pickle', 'wb') as handle:\n",
    "    pickle.dump(tokenizer, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
    "    \n",
    "# save encoder\n",
    "with open('encoder.pickle', 'wb') as handle:\n",
    "    pickle.dump(encoder, handle, protocol=pickle.HIGHEST_PROTOCOL)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# deploy the model\n",
    "# user give the title and the model will predict the source\n",
    "# Load the model and tokenizer\n",
    "from tensorflow.keras.models import load_model\n",
    "import pickle\n",
    "\n",
    "# Load the tokenizer\n",
    "with open('tokenizer.pickle', 'rb') as handle:\n",
    "    tokenizer = pickle.load(handle)\n",
    "\n",
    "# Load the encoder\n",
    "with open('encoder.pickle', 'rb') as handle:\n",
    "    encoder = pickle.load(handle)\n",
    "\n",
    "\n",
    "def predict_source(title):\n",
    "    # Load the model\n",
    "    model = load_model('news_classifier.h5')\n",
    "    # Tokenize the input\n",
    "    X = tokenizer.texts_to_sequences([title])\n",
    "    X = pad_sequences(X)\n",
    "    # Predict the source\n",
    "    y_pred = model.predict(X)\n",
    "    source = encoder.inverse_transform(y_pred.argmax(axis=1))\n",
    "    return source[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step\n",
      "Predicted Source: foxnews\n"
     ]
    }
   ],
   "source": [
    "# Test the function\n",
    "# user input\n",
    "title = input(\"Enter the title: \")\n",
    "source = predict_source(title)\n",
    "print(f\"Predicted Source: {source}\")"
   ]
  }
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