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   "cell_type": "code",
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   "source": [
    "# Step 1: Login to Hugging Face\n",
    "\n",
    "from huggingface_hub import login\n",
    "login(\"hf_XXXXJsPbaZ\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Fzuvi5bOKxFS",
    "trusted": true
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   "source": [
    "# Step 2: Install required libraries\n",
    "!pip install accelerate\n",
    "!pip install python-docx\n",
    "!pip install PyPDF2\n",
    "!pip install ipywidgets\n",
    "!apt-get install -y antiword"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "id": "emXo05ZVIVvq",
    "outputId": "f64c4eae-99ba-4fd7-b1af-f52c35ea818b",
    "trusted": true
   },
   "outputs": [],
   "source": [
    "# Step 3: Load the Gemma-3-1b-it model from Hugging Face\n",
    "# https://huggingface.co/google/gemma-3-1b-it\n",
    "\n",
    "import torch\n",
    "import torch._dynamo\n",
    "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
    "\n",
    "\n",
    "model_id = \"google/gemma-3-1b-it\"\n",
    "\n",
    "# ✅ Use float16 for Tesla T4 to reduce memory\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
    "model = AutoModelForCausalLM.from_pretrained(\n",
    "    model_id,\n",
    "    device_map=\"auto\",\n",
    "    torch_dtype=torch.float16\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "trusted": true
   },
   "outputs": [],
   "source": [
    "# Step 4: Configure CUDA backend and verify model device\n",
    "\n",
    "# Disable memory-efficient and flash attention (optional for debugging or compatibility)\n",
    "torch.backends.cuda.enable_mem_efficient_sdp(True)\n",
    "torch.backends.cuda.enable_flash_sdp(True)\n",
    "\n",
    "# Print which device (CPU/GPU) the model is loaded on\n",
    "print(f\"Model device: {model.device}\")\n",
    "\n",
    "# Confirm that setup is complete\n",
    "print(\"Run complete\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "trusted": true
   },
   "outputs": [],
   "source": [
    "# ==========================\n",
    "# STEP 5: Define File Reading & Text Extraction Functions\n",
    "# ==========================\n",
    "\n",
    "import os\n",
    "import docx\n",
    "from PyPDF2 import PdfReader\n",
    "import subprocess\n",
    "\n",
    "# --- Function: Extract text from DOCX files ---\n",
    "def extract_text_from_docx(docx_path):\n",
    "    text = \"\"\n",
    "    doc = docx.Document(docx_path)\n",
    "    for paragraph in doc.paragraphs:\n",
    "        text += paragraph.text + \"\\n\"\n",
    "    return text.strip()\n",
    "\n",
    "# --- Function: Extract text from PDF files ---\n",
    "def extract_text_from_pdf(pdf_path):\n",
    "    text = \"\"\n",
    "    with open(pdf_path, \"rb\") as file:\n",
    "        reader = PdfReader(file)\n",
    "        for page in reader.pages:\n",
    "            if page.extract_text():\n",
    "                text += page.extract_text() + \"\\n\"\n",
    "    return text.strip()\n",
    "\n",
    "# --- Function: Extract text from DOC (97–2003) using antiword ---\n",
    "def extract_text_from_doc(doc_path):\n",
    "    try:\n",
    "        result = subprocess.run([\"antiword\", doc_path], capture_output=True, text=True)\n",
    "        return result.stdout.strip()\n",
    "    except Exception as e:\n",
    "        return f\"❌ Error reading DOC file {doc_path}: {e}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "trusted": true
   },
   "outputs": [],
   "source": [
    "# ==========================\n",
    "# STEP 6 : Define response generation function\n",
    "# ==========================\n",
    "\n",
    "import time\n",
    "\n",
    "def generate_respone(contract_content):\n",
    "    \"\"\"\n",
    "    Extracts key contract information using the language model.\n",
    "    Each category is queried separately for precise output.\n",
    "    \"\"\"\n",
    "    list_of_categories = [\n",
    "        'Contract Date', 'Effective Date', 'Renewal Term',\n",
    "        'Exit clause incl. notice period', 'Contract Parties',\n",
    "        'Documents Retention Period', 'Audit Clause', 'Audit Frequency',\n",
    "        'Audit Duration', 'Contract Fees', 'Payment Terms',\n",
    "        'Contract scope', 'KPIs', 'Service Level Agreement',\n",
    "        'Roles & Responsibilities', 'Deliverables', 'Contractual Reporting',\n",
    "        'Performance Review', 'Performance Bonus',\n",
    "        'Data Protection and Privacy', 'Confidentiality'\n",
    "    ]\n",
    "\n",
    "    save_dict = {}\n",
    "    start_time = time.time()\n",
    "\n",
    "    for cate in list_of_categories:\n",
    "        prompt = (\n",
    "            f\"Extract only the exact information about '{cate}' from the following contract. \"\n",
    "            \"Do not explain, summarize, or rephrase. If not found, answer 'None'.\\n\\nContract:\\n\"\n",
    "            + contract_content\n",
    "        )\n",
    "\n",
    "        chat = [{\"role\": \"user\", \"content\": prompt}]\n",
    "        chat_template = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)\n",
    "        inputs = tokenizer.encode(chat_template, add_special_tokens=False, return_tensors=\"pt\")\n",
    "\n",
    "        outputs = model.generate(\n",
    "            input_ids=inputs.to(model.device),\n",
    "            do_sample=True,\n",
    "            temperature=0.3,\n",
    "            top_p=0.9,\n",
    "            max_new_tokens=512\n",
    "        )\n",
    "\n",
    "        string = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
    "        cut_string = string.split(\"model\")[-1].strip()\n",
    "        cut_string = cut_string.replace(\"Here’s a breakdown\", \"\").replace(\"Here's a breakdown\", \"\").strip()\n",
    "\n",
    "        save_dict[cate] = cut_string\n",
    "\n",
    "    print(\"✅ Extraction completed in\", round(time.time() - start_time, 2), \"seconds\")\n",
    "    return save_dict\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "trusted": true
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   "outputs": [],
   "source": [
    "# Step 6.2: Clean and organize extracted contract information\n",
    "\n",
    "import re\n",
    "import pandas as pd \n",
    "\n",
    "def clean_respone(respone, filename):\n",
    "\n",
    "    cates = []\n",
    "    values = []\n",
    "    \n",
    "    # Loop through all extracted information\n",
    "    for key, value in respone.items():\n",
    "        \n",
    "        # Split text into sentences\n",
    "        sentences = re.split(r'(?<!\\w\\.\\w.)(?<![A-Z][a-z]\\.)(?<=\\.|\\?|:)\\s', value)\n",
    "    \n",
    "        # Remove irrelevant filler sentences\n",
    "        filtered_sentences = [\n",
    "            sentence for sentence in sentences \n",
    "            if \"cannot provide\".lower() not in sentence.lower() and\n",
    "               \"cannot extract\".lower() not in sentence.lower() and\n",
    "               \"the text does not contain\".lower() not in sentence.lower() and\n",
    "               \"sure, the information related\".lower() not in sentence.lower() and\n",
    "               \"sure, here is information\".lower() not in sentence.lower() and\n",
    "               \"sure, here is the information\".lower() not in sentence.lower()\n",
    "        ]\n",
    "    \n",
    "        # Join filtered sentences\n",
    "        filtered_text = ' '.join(filtered_sentences)\n",
    "        if filtered_text.strip() == \"\":\n",
    "            filtered_text = \"None\"\n",
    "    \n",
    "        # Clean up extra tokens and formatting\n",
    "        clean_text = filtered_text.replace('<eos>', '').replace('\\n', ' ').strip()\n",
    "    \n",
    "        cates.append(key)\n",
    "        values.append(clean_text)\n",
    "    \n",
    "    # ✅ Create a clean, organized DataFrame\n",
    "    data = pd.DataFrame({\n",
    "        \"Contract name\": file_name,           # <-- Auto-filled real file name\n",
    "        \"Contract Information\": cates,\n",
    "        \"Details\": values\n",
    "    })\n",
    "    \n",
    "    # Display the result\n",
    "    return data"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "trusted": true
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   "source": [
    "# ==========================\n",
    "# STEP 7 : Read contract files and generate responses\n",
    "# ==========================\n",
    "\n",
    "# Initialize an empty list to store all response DataFrames\n",
    "dfs = []\n",
    "\n",
    "# Define the base directory where all contract files are stored\n",
    "base_dir = \"/kaggle/input/contracts\" \n",
    "\n",
    "# Iterate through each file in the dataset directory\n",
    "for file in os.listdir(base_dir):\n",
    "\n",
    "    file_path = os.path.join(base_dir, file)\n",
    "    file_name = os.path.basename(file).split('.')[0]\n",
    "\n",
    "    print(f\"Processing file: {file}\")\n",
    "    \n",
    "    # --- Detect file type and extract text accordingly ---\n",
    "    if file_path.split(\".\")[-1] == 'docx':\n",
    "        print(\"📘 DOCX file found.\")\n",
    "        docx_text = extract_text_from_docx(file_path)\n",
    "        contract_content = docx_text\n",
    "    \n",
    "    elif file_path.split(\".\")[-1] == 'pdf':\n",
    "        print(\"📕 PDF file found.\")\n",
    "        pdf_text = extract_text_from_pdf(file_path)\n",
    "        contract_content = pdf_text\n",
    "\n",
    "    elif file_path.split(\".\")[-1] == \"doc\":\n",
    "        print(\"📗 DOC (97-2003) file found.\")\n",
    "        doc_text = extract_text_from_doc(file_path)\n",
    "        contract_content = doc_text\n",
    "    \n",
    "    else:\n",
    "        print(\"⚠️ File type not recognized. Skipping this file.\")\n",
    "        continue\n",
    "\n",
    "    # --- Step 1: Extraction completed ---\n",
    "    print(\"✅ Text extraction complete!\")\n",
    "\n",
    "    # --- Step 2: Generate model response for this contract ---\n",
    "    print(\"🧠 Generating model response ...\")\n",
    "    respone = generate_respone(contract_content)\n",
    "    print(\"✅ Response generation complete!\")\n",
    "\n",
    "    # --- Step 3: Clean and structure the model response ---\n",
    "    print(\"🧹 Cleaning model response ...\")\n",
    "    respone_data = clean_respone(respone, file_name)\n",
    "    print(\"✅ Response cleaning complete!\")\n",
    "    print(\"=\"*50)\n",
    "\n",
    "    # Append the cleaned response to the main list\n",
    "    dfs.append(respone_data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "trusted": true
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   "source": [
    "# ==========================\n",
    "# STEP 8 : Combine, Format, and Display Final Results\n",
    "# ==========================\n",
    "\n",
    "import pandas as pd\n",
    "from IPython.display import display, HTML\n",
    "\n",
    "# 🧩 Configure pandas display options\n",
    "pd.set_option('display.max_rows', 50)           # Limit rows displayed\n",
    "pd.set_option('display.max_columns', 50)        # Limit columns displayed\n",
    "pd.set_option('display.max_colwidth', 200)      # Allow wider text before truncating\n",
    "\n",
    "# 🧠 Combine all response DataFrames into a single table\n",
    "final_df_respone = pd.concat(dfs, ignore_index=True)\n",
    "\n",
    "print(\"✅ All responses combined successfully!\")\n",
    "print(f\"Total contracts processed: {len(dfs)}\")\n",
    "print(\"------------------------------------------------------------\")\n",
    "\n",
    "# 🎨 Create custom CSS for better table visualization\n",
    "custom_css = \"\"\"\n",
    "<style>\n",
    "table {\n",
    "    border-collapse: collapse;\n",
    "    width: 100%;\n",
    "    table-layout: fixed;  /* Keep columns consistent in width */\n",
    "    word-wrap: break-word;  /* Allow wrapping within cells */\n",
    "}\n",
    "th, td {\n",
    "    border: 1px solid #ddd;\n",
    "    text-align: left;       /* Align text to the left */\n",
    "    padding: 10px;          /* Add spacing for readability */\n",
    "    vertical-align: top;    /* Align text to top of each cell */\n",
    "}\n",
    "th {\n",
    "    background-color: #f7f7f7;\n",
    "    font-weight: bold;\n",
    "}\n",
    "tr:nth-child(even) {background-color: #fafafa;}\n",
    "</style>\n",
    "\"\"\"\n",
    "\n",
    "# 👀 Display scrollable HTML table with clean formatting\n",
    "print(\"📋 Formatted full table preview (scroll horizontally if needed):\")\n",
    "html_table = final_df_respone.to_html(index=False, escape=False)\n",
    "display(HTML(custom_css + f\"\"\"\n",
    "<div style=\"overflow-x: auto; max-width: 100%; border:1px solid #ddd; padding:12px;\">\n",
    "{html_table}\n",
    "</div>\n",
    "\"\"\"))\n",
    "\n",
    "# 💾 Save to Excel for full offline review\n",
    "output_path = \"/kaggle/working/final_contract_summary.xlsx\"\n",
    "final_df_respone.to_excel(output_path, index=False)\n",
    "\n",
    "print(f\"✅ Saved formatted results to: {output_path}\")\n",
    "print(\"📂 You can download this Excel file from the Kaggle 'working' directory.\")\n"
   ]
  }
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