{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "notebook-header",
   "metadata": {},
   "source": [
    "# Model Testing Notebook\n",
    "## Smart Summarizer - Testing TextRank, BART, and PEGASUS Models\n",
    "\n",
    "**Author**: Abdul Razzaq Ansari  \n",
    "**Student ID**: 23049149  \n",
    "**Module**: CU6051NI Artificial Intelligence  \n",
    "**Institution**: London Metropolitan University\n",
    "\n",
    "This notebook tests the three summarization models individually and compares their performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "setup-imports",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✓ All imports successful\n"
     ]
    }
   ],
   "source": [
    "# Setup and imports\n",
    "import sys\n",
    "import os\n",
    "sys.path.append('..')\n",
    "\n",
    "# Core imports\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import time\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Model imports\n",
    "from models.textrank import TextRankSummarizer\n",
    "from models.bart import BARTSummarizer\n",
    "from models.pegasus import PEGASUSSummarizer\n",
    "\n",
    "# Utility imports\n",
    "from utils.evaluator import SummarizerEvaluator\n",
    "from utils.data_loader import DataLoader\n",
    "\n",
    "# Set style\n",
    "plt.style.use('seaborn-v0_8')\n",
    "sns.set_palette(\"husl\")\n",
    "\n",
    "print(\"✓ All imports successful\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "32778fbb-7d4f-4dd8-87c8-53d9ed77ae69",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample text loaded\n",
      "Original length: 195 words\n",
      "Reference summary: 43 words\n"
     ]
    }
   ],
   "source": [
    "sample_text = \"\"\"\n",
    "Artificial intelligence has revolutionized modern technology in unprecedented ways. \n",
    "Machine learning algorithms enable computers to learn from vast amounts of data without \n",
    "explicit programming. Deep learning neural networks, inspired by the human brain, can \n",
    "now recognize patterns in images, understand natural language, and even generate creative \n",
    "content. Natural language processing has advanced to the point where AI systems can \n",
    "engage in human-like conversations, translate between languages in real-time, and \n",
    "summarize lengthy documents automatically. Computer vision technology allows machines \n",
    "to interpret and understand visual information from the world, powering applications \n",
    "from autonomous vehicles to medical diagnosis systems. The integration of AI across \n",
    "industries has improved efficiency, accuracy, and decision-making capabilities. \n",
    "Healthcare providers use AI to detect diseases earlier and recommend personalized \n",
    "treatments. Financial institutions employ machine learning for fraud detection and \n",
    "algorithmic trading. Manufacturing companies utilize AI-powered robots for precision \n",
    "tasks and quality control. Despite these advances, challenges remain in areas such as \n",
    "algorithmic bias, data privacy, interpretability of AI decisions, and the ethical \n",
    "implications of autonomous systems. Researchers continue to work on developing more \n",
    "transparent, fair, and robust AI systems that can be safely deployed across diverse \n",
    "applications while maintaining human oversight and ethical standards.\n",
    "\"\"\"\n",
    "\n",
    "reference_summary = \"\"\"\n",
    "AI has transformed technology through machine learning and deep learning. Natural language \n",
    "processing enables human-like interactions and automatic summarization. Computer vision \n",
    "powers autonomous vehicles and medical diagnosis. AI improves efficiency across healthcare, \n",
    "finance, and manufacturing. Challenges include algorithmic bias, privacy, and ethical concerns.\n",
    "\"\"\"\n",
    "\n",
    "print(\"Sample text loaded\")\n",
    "print(f\"Original length: {len(sample_text.split())} words\")\n",
    "print(f\"Reference summary: {len(reference_summary.split())} words\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "677e0c21-9ac1-4782-ade3-ebd5cde16354",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:models.base_summarizer:Initializing TextRank (Extractive) summarizer\n",
      "INFO:models.textrank:TextRank summarizer initialized successfully\n",
      "INFO:models.base_summarizer:Initializing BART (Abstractive) summarizer\n",
      "INFO:models.bart:Loading BART model: facebook/bart-large-cnn\n",
      "INFO:models.bart:Initial model loading may take 2-3 minutes...\n",
      "INFO:models.bart:Using device: cpu\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initializing models...\n",
      "\n",
      "1. Loading TextRank...\n",
      "   ✓ TextRank ready\n",
      "\n",
      "2. Loading BART (this may take a moment)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:models.bart:BART model loaded successfully!\n",
      "INFO:models.base_summarizer:Initializing PEGASUS (Abstractive) summarizer\n",
      "INFO:models.pegasus:Loading PEGASUS model: google/pegasus-cnn_dailymail\n",
      "INFO:models.pegasus:PEGASUS is a large model. Initial loading may take 3-5 minutes...\n",
      "INFO:models.pegasus:Using device: cpu\n",
      "INFO:models.pegasus:Loading tokenizer...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   ✓ BART ready\n",
      "\n",
      "3. Loading PEGASUS (this may take a moment)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:models.pegasus:Loading model weights...\n",
      "Some weights of PegasusForConditionalGeneration were not initialized from the model checkpoint at google/pegasus-cnn_dailymail and are newly initialized: ['model.decoder.embed_positions.weight', 'model.encoder.embed_positions.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
      "INFO:models.pegasus:PEGASUS model loaded successfully!\n",
      "INFO:models.pegasus:Model size: 568.7M parameters\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   ✓ PEGASUS ready\n",
      "\n",
      "✓ All models loaded successfully!\n"
     ]
    }
   ],
   "source": [
    "print(\"Initializing models...\\n\")\n",
    "\n",
    "print(\"1. Loading TextRank...\")\n",
    "textrank = TextRankSummarizer()\n",
    "print(\"   ✓ TextRank ready\\n\")\n",
    "\n",
    "print(\"2. Loading BART (this may take a moment)...\")\n",
    "bart = BARTSummarizer(device='cpu')\n",
    "print(\"   ✓ BART ready\\n\")\n",
    "\n",
    "print(\"3. Loading PEGASUS (this may take a moment)...\")\n",
    "pegasus = PEGASUSSummarizer(device='cpu')\n",
    "print(\"   ✓ PEGASUS ready\\n\")\n",
    "\n",
    "print(\"✓ All models loaded successfully!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "59534dc9-91d9-46bf-a47e-b79eb69ad2c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating summaries...\n",
      "\n",
      "======================================================================\n",
      "\n",
      "1. TEXTRANK (Extractive)\n",
      "----------------------------------------------------------------------\n",
      "Summary: Natural language processing has advanced to the point where AI systems can \n",
      "engage in human-like conversations, translate between languages in real-time, and \n",
      "summarize lengthy documents automatically. Despite these advances, challenges remain in areas such as \n",
      "algorithmic bias, data privacy, interpretability of AI decisions, and the ethical \n",
      "implications of autonomous systems. Researchers continue to work on developing more \n",
      "transparent, fair, and robust AI systems that can be safely deployed across diverse \n",
      "applications while maintaining human oversight and ethical standards.\n",
      "\n",
      "Metadata:\n",
      "  - Processing time: 0.0330s\n",
      "  - Summary length: 78 words\n",
      "  - Compression: 40.00%\n",
      "\n",
      "======================================================================\n",
      "\n",
      "2. BART (Abstractive)\n",
      "----------------------------------------------------------------------\n",
      "Summary: Artificial intelligence has revolutionized modern technology in unprecedented ways. Machine learning algorithms enable computers to learn from vast amounts of data. Deep learning neural networks, inspired by the human brain, can recognize patterns in images, understand natural language, and even generate creative content.\n",
      "\n",
      "Metadata:\n",
      "  - Processing time: 11.2542s\n",
      "  - Summary length: 43 words\n",
      "  - Compression: 22.05%\n",
      "\n",
      "======================================================================\n",
      "\n",
      "3. PEGASUS (Abstractive)\n",
      "----------------------------------------------------------------------\n",
      "Summary: Machine learning algorithms enable computers to learn from vast amounts of data without explicit programming.<n>Deep learning neural networks, inspired by the human brain, can now recognize patterns in images, understand natural language, and even generate creative content.<n>Computer vision technology allows machines to interpret and understand visual information from the world.\n",
      "\n",
      "Metadata:\n",
      "  - Processing time: 15.3631s\n",
      "  - Summary length: 50 words\n",
      "  - Compression: 25.64%\n",
      "\n",
      "======================================================================\n"
     ]
    }
   ],
   "source": [
    "print(\"Generating summaries...\\n\")\n",
    "print(\"=\"*70)\n",
    "\n",
    "# TextRank\n",
    "print(\"\\n1. TEXTRANK (Extractive)\")\n",
    "print(\"-\"*70)\n",
    "start = time.time()\n",
    "tr_result = textrank.summarize_with_metrics(sample_text)\n",
    "tr_time = time.time() - start\n",
    "\n",
    "print(f\"Summary: {tr_result['summary']}\")\n",
    "print(f\"\\nMetadata:\")\n",
    "print(f\"  - Processing time: {tr_time:.4f}s\")\n",
    "print(f\"  - Summary length: {tr_result['metadata']['summary_length']} words\")\n",
    "print(f\"  - Compression: {tr_result['metadata']['compression_ratio']:.2%}\")\n",
    "\n",
    "# BART\n",
    "print(\"\\n\" + \"=\"*70)\n",
    "print(\"\\n2. BART (Abstractive)\")\n",
    "print(\"-\"*70)\n",
    "start = time.time()\n",
    "bart_result = bart.summarize_with_metrics(sample_text, max_length=100, min_length=30)\n",
    "bart_time = time.time() - start\n",
    "\n",
    "print(f\"Summary: {bart_result['summary']}\")\n",
    "print(f\"\\nMetadata:\")\n",
    "print(f\"  - Processing time: {bart_time:.4f}s\")\n",
    "print(f\"  - Summary length: {bart_result['metadata']['summary_length']} words\")\n",
    "print(f\"  - Compression: {bart_result['metadata']['compression_ratio']:.2%}\")\n",
    "\n",
    "# PEGASUS\n",
    "print(\"\\n\" + \"=\"*70)\n",
    "print(\"\\n3. PEGASUS (Abstractive)\")\n",
    "print(\"-\"*70)\n",
    "start = time.time()\n",
    "peg_result = pegasus.summarize_with_metrics(sample_text, max_length=100, min_length=30)\n",
    "peg_time = time.time() - start\n",
    "\n",
    "print(f\"Summary: {peg_result['summary']}\")\n",
    "print(f\"\\nMetadata:\")\n",
    "print(f\"  - Processing time: {peg_time:.4f}s\")\n",
    "print(f\"  - Summary length: {peg_result['metadata']['summary_length']} words\")\n",
    "print(f\"  - Compression: {peg_result['metadata']['compression_ratio']:.2%}\")\n",
    "\n",
    "print(\"\\n\" + \"=\"*70)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "83aa6bd4-3105-40a6-acb5-25f02de0fc96",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:utils.evaluator:Evaluator initialized with rouge library\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluating against reference summary...\n",
      "\n",
      "   Model  ROUGE-1  ROUGE-2  ROUGE-L  Time (s)  Words  Compression\n",
      "TextRank 0.254902 0.067227 0.235294  0.032988     78     0.400000\n",
      "    BART 0.101266 0.000000 0.101266 11.254168     43     0.220513\n",
      " PEGASUS 0.095238 0.000000 0.095238 15.363073     50     0.256410\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nEvaluating against reference summary...\\n\")\n",
    "\n",
    "evaluator = SummarizerEvaluator()\n",
    "\n",
    "# Evaluate each model\n",
    "tr_scores = evaluator.evaluate_single(tr_result['summary'], reference_summary, \"TextRank\")\n",
    "bart_scores = evaluator.evaluate_single(bart_result['summary'], reference_summary, \"BART\")\n",
    "peg_scores = evaluator.evaluate_single(peg_result['summary'], reference_summary, \"PEGASUS\")\n",
    "\n",
    "# Create comparison DataFrame\n",
    "comparison_df = pd.DataFrame([\n",
    "    {\n",
    "        'Model': 'TextRank',\n",
    "        'ROUGE-1': tr_scores['rouge_1_f1'],\n",
    "        'ROUGE-2': tr_scores['rouge_2_f1'],\n",
    "        'ROUGE-L': tr_scores['rouge_l_f1'],\n",
    "        'Time (s)': tr_time,\n",
    "        'Words': tr_result['metadata']['summary_length'],\n",
    "        'Compression': tr_result['metadata']['compression_ratio']\n",
    "    },\n",
    "    {\n",
    "        'Model': 'BART',\n",
    "        'ROUGE-1': bart_scores['rouge_1_f1'],\n",
    "        'ROUGE-2': bart_scores['rouge_2_f1'],\n",
    "        'ROUGE-L': bart_scores['rouge_l_f1'],\n",
    "        'Time (s)': bart_time,\n",
    "        'Words': bart_result['metadata']['summary_length'],\n",
    "        'Compression': bart_result['metadata']['compression_ratio']\n",
    "    },\n",
    "    {\n",
    "        'Model': 'PEGASUS',\n",
    "        'ROUGE-1': peg_scores['rouge_1_f1'],\n",
    "        'ROUGE-2': peg_scores['rouge_2_f1'],\n",
    "        'ROUGE-L': peg_scores['rouge_l_f1'],\n",
    "        'Time (s)': peg_time,\n",
    "        'Words': peg_result['metadata']['summary_length'],\n",
    "        'Compression': peg_result['metadata']['compression_ratio']\n",
    "    }\n",
    "])\n",
    "\n",
    "print(comparison_df.to_string(index=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4c130551-5528-4246-9e01-97c5c660622d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 2, figsize=(15, 10))\n",
    "\n",
    "# ROUGE Scores\n",
    "rouge_df = comparison_df[['Model', 'ROUGE-1', 'ROUGE-2', 'ROUGE-L']].set_index('Model')\n",
    "rouge_df.plot(kind='bar', ax=axes[0, 0], width=0.8)\n",
    "axes[0, 0].set_title('ROUGE Score Comparison', fontsize=14, fontweight='bold')\n",
    "axes[0, 0].set_ylabel('F1 Score')\n",
    "axes[0, 0].set_ylim([0, 1])\n",
    "axes[0, 0].legend(title='Metrics')\n",
    "axes[0, 0].grid(axis='y', alpha=0.3)\n",
    "\n",
    "# Processing Time\n",
    "axes[0, 1].bar(comparison_df['Model'], comparison_df['Time (s)'], \n",
    "               color=['#FF6B6B', '#4ECDC4', '#45B7D1'])\n",
    "axes[0, 1].set_title('Processing Time Comparison', fontsize=14, fontweight='bold')\n",
    "axes[0, 1].set_ylabel('Time (seconds)')\n",
    "axes[0, 1].grid(axis='y', alpha=0.3)\n",
    "\n",
    "# Summary Length\n",
    "axes[1, 0].bar(comparison_df['Model'], comparison_df['Words'],\n",
    "               color=['#FF6B6B', '#4ECDC4', '#45B7D1'])\n",
    "axes[1, 0].set_title('Summary Length Comparison', fontsize=14, fontweight='bold')\n",
    "axes[1, 0].set_ylabel('Number of Words')\n",
    "axes[1, 0].grid(axis='y', alpha=0.3)\n",
    "\n",
    "# Compression Ratio\n",
    "axes[1, 1].bar(comparison_df['Model'], comparison_df['Compression'],\n",
    "               color=['#FF6B6B', '#4ECDC4', '#45B7D1'])\n",
    "axes[1, 1].set_title('Compression Ratio', fontsize=14, fontweight='bold')\n",
    "axes[1, 1].set_ylabel('Ratio (Summary/Original)')\n",
    "axes[1, 1].grid(axis='y', alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "871b5e06-91b8-44da-8432-ebc7657558e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "======================================================================\n",
      "DETAILED ANALYSIS\n",
      "======================================================================\n",
      "\n",
      "🏆 Best ROUGE-1 Score: TextRank\n",
      "🏆 Best ROUGE-2 Score: TextRank\n",
      "⚡ Fastest Processing: TextRank\n",
      "\n",
      "📊 Key Observations:\n",
      "1. Quality: TextRank and TextRank provide best quality\n",
      "2. Speed: TextRank is 465.7x faster\n",
      "3. TextRank is extractive (uses original sentences)\n",
      "4. BART and PEGASUS generate new text (abstractive)\n",
      "\n",
      "💡 Recommendations:\n",
      "- Use TextRank for: Fast processing, resource-constrained environments\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n\" + \"=\"*70)\n",
    "print(\"DETAILED ANALYSIS\")\n",
    "print(\"=\"*70)\n",
    "\n",
    "best_rouge1 = comparison_df.loc[comparison_df['ROUGE-1'].idxmax(), 'Model']\n",
    "best_rouge2 = comparison_df.loc[comparison_df['ROUGE-2'].idxmax(), 'Model']\n",
    "fastest = comparison_df.loc[comparison_df['Time (s)'].idxmin(), 'Model']\n",
    "\n",
    "print(f\"\\n🏆 Best ROUGE-1 Score: {best_rouge1}\")\n",
    "print(f\"🏆 Best ROUGE-2 Score: {best_rouge2}\")\n",
    "print(f\"⚡ Fastest Processing: {fastest}\")\n",
    "\n",
    "print(\"\\n📊 Key Observations:\")\n",
    "print(f\"1. Quality: {best_rouge1} and {best_rouge2} provide best quality\")\n",
    "print(f\"2. Speed: {fastest} is {comparison_df['Time (s)'].max()/comparison_df['Time (s)'].min():.1f}x faster\")\n",
    "print(f\"3. TextRank is extractive (uses original sentences)\")\n",
    "print(f\"4. BART and PEGASUS generate new text (abstractive)\")\n",
    "\n",
    "print(\"\\n💡 Recommendations:\")\n",
    "if fastest == \"TextRank\":\n",
    "    print(\"- Use TextRank for: Fast processing, resource-constrained environments\")\n",
    "if best_rouge1 in [\"BART\", \"PEGASUS\"]:\n",
    "    print(f\"- Use {best_rouge1} for: Highest quality, when resources available\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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