Chain_Of_Thought.ipynb

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          ]
        },
        "id": "tjrWo2gM8-K2",
        "outputId": "131b23ce-7713-4966-aabc-27f87e910eda"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "525210c2bad74412acbc68d11e97be1a"
            }
          },
          "metadata": {}
        },
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "c14c2e8843f949758cf7e5d0f3f4e5ab"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/microsoft/Phi-3-mini-4k-instruct:\n",
            "- configuration_phi3.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "c29184e4eb724c6a9fd3395ad26fcde1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/microsoft/Phi-3-mini-4k-instruct:\n",
            "- modeling_phi3.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n",
            "WARNING:transformers_modules.microsoft.Phi-3-mini-4k-instruct.0a67737cc96d2554230f90338b163bc6380a2a85.modeling_phi3:`flash-attention` package not found, consider installing for better performance: No module named 'flash_attn'.\n",
            "WARNING:transformers_modules.microsoft.Phi-3-mini-4k-instruct.0a67737cc96d2554230f90338b163bc6380a2a85.modeling_phi3:Current `flash-attention` does not support `window_size`. Either upgrade or use `attn_implementation='eager'`.\n"
          ]
        },
        {
          "output_type": "display_data",
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            }
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          "metadata": {}
        },
        {
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            ],
            "application/vnd.jupyter.widget-view+json": {
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            }
          },
          "metadata": {}
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            "application/vnd.jupyter.widget-view+json": {
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            }
          },
          "metadata": {}
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            }
          },
          "metadata": {}
        },
        {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
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            "application/vnd.jupyter.widget-view+json": {
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            }
          },
          "metadata": {}
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          },
          "metadata": {}
        },
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0bb0adbcd3124e3b81b3ff0b659e0cff"
            }
          },
          "metadata": {}
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        {
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            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "c98fd01cf4d7428dbe500924fd3b2a1b"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "import torch\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline\n",
        "\n",
        "# Load model and tokenizer\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    \"microsoft/Phi-3-mini-4k-instruct\",\n",
        "    device_map=\"cuda\",\n",
        "    torch_dtype=\"auto\",\n",
        "    trust_remote_code=True,\n",
        ")\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"microsoft/Phi-3-mini-4k-instruct\")\n",
        "\n",
        "# Create a pipeline\n",
        "pipe = pipeline(\n",
        "    \"text-generation\",\n",
        "    model=model,\n",
        "    tokenizer=tokenizer,\n",
        "    return_full_text=False,\n",
        "    max_new_tokens=500,\n",
        "    do_sample=False,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install sentence-transformers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f9uCnSYd8_q6",
        "outputId": "000af45d-086d-4f80-eaf6-e83aae135e8b"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting sentence-transformers\n",
            "  Downloading sentence_transformers-3.2.0-py3-none-any.whl.metadata (10 kB)\n",
            "Requirement already satisfied: transformers<5.0.0,>=4.41.0 in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (4.44.2)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (4.66.5)\n",
            "Requirement already satisfied: torch>=1.11.0 in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (2.4.1+cu121)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (1.5.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (1.13.1)\n",
            "Requirement already satisfied: huggingface-hub>=0.20.0 in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (0.24.7)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (10.4.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.20.0->sentence-transformers) (3.16.1)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.20.0->sentence-transformers) (2024.6.1)\n",
            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.20.0->sentence-transformers) (24.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.20.0->sentence-transformers) (6.0.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.20.0->sentence-transformers) (2.32.3)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.20.0->sentence-transformers) (4.12.2)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence-transformers) (1.13.3)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence-transformers) (3.4.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence-transformers) (3.1.4)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.41.0->sentence-transformers) (1.26.4)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.41.0->sentence-transformers) (2024.9.11)\n",
            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.41.0->sentence-transformers) (0.4.5)\n",
            "Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.41.0->sentence-transformers) (0.19.1)\n",
            "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->sentence-transformers) (1.4.2)\n",
            "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->sentence-transformers) (3.5.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.11.0->sentence-transformers) (3.0.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.20.0->sentence-transformers) (3.4.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.20.0->sentence-transformers) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.20.0->sentence-transformers) (2.2.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.20.0->sentence-transformers) (2024.8.30)\n",
            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.11.0->sentence-transformers) (1.3.0)\n",
            "Downloading sentence_transformers-3.2.0-py3-none-any.whl (255 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m255.2/255.2 kB\u001b[0m \u001b[31m6.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: sentence-transformers\n",
            "Successfully installed sentence-transformers-3.2.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from dataclasses import dataclass\n",
        "from typing import List, Dict, Optional, Tuple\n",
        "import json\n",
        "from datetime import datetime\n",
        "from sentence_transformers import SentenceTransformer\n",
        "import numpy as np\n",
        "from enum import Enum\n",
        "\n",
        "class RiskLevel(Enum):\n",
        "    LOW = \"Low\"\n",
        "    MEDIUM = \"Medium\"\n",
        "    HIGH = \"High\"\n",
        "    CRITICAL = \"Critical\"\n",
        "\n",
        "    def to_dict(self):\n",
        "        return {'level': self.name, 'description': self.value}\n",
        "\n",
        "@dataclass\n",
        "class SecurityDomain:\n",
        "    name: str\n",
        "    keywords: List[str]\n",
        "    best_practices: List[str]\n",
        "    risk_indicators: List[str]\n",
        "    follow_up_templates: List[str]\n",
        "\n",
        "@dataclass\n",
        "class SecurityResponse:\n",
        "    question: str\n",
        "    answer: str\n",
        "    domain: str\n",
        "    risk_score: float\n",
        "    risk_level: RiskLevel\n",
        "    timestamp: str\n",
        "    recommendations: List[str]\n",
        "    reasoning: str\n",
        "\n",
        "class DynamicSecurityConversationManager:\n",
        "    def __init__(self, config_path: str = \"/content/knowledge_base.json\"):\n",
        "        self.conversation_history: List[SecurityResponse] = []\n",
        "        self.risk_scores: Dict[str, List[float]] = {}\n",
        "        self.encoder = SentenceTransformer('all-mpnet-base-v2')\n",
        "        self.question_count = {domain: 0 for domain in self._load_domains(config_path)}\n",
        "\n",
        "        # Load configuration from JSON\n",
        "        with open(config_path, 'r') as f:\n",
        "            config = json.load(f)\n",
        "\n",
        "        # Initialize security domains from configuration\n",
        "        self.security_domains = {}\n",
        "        for domain_name, domain_data in config['domains'].items():\n",
        "            self.security_domains[domain_name] = SecurityDomain(\n",
        "                name=domain_data['name'],\n",
        "                keywords=domain_data['keywords'],\n",
        "                best_practices=domain_data['best_practices'],\n",
        "                risk_indicators=domain_data['risk_indicators'],\n",
        "                follow_up_templates=domain_data['follow_up_templates']\n",
        "            )\n",
        "\n",
        "        # Store risk thresholds\n",
        "        self.domain_risk_thresholds = config['risk_thresholds']\n",
        "\n",
        "    def _load_domains(self, config_path: str) -> List[str]:\n",
        "        \"\"\"\n",
        "        Load security domains from JSON configuration.\n",
        "        \"\"\"\n",
        "        with open(config_path, 'r') as f:\n",
        "            config = json.load(f)\n",
        "        return list(config['domains'].keys())\n",
        "\n",
        "    def process_response(self, question: str, answer: str) -> Dict:\n",
        "        \"\"\"\n",
        "        Process a security-related response and generate analysis.\n",
        "\n",
        "        Args:\n",
        "            question: The security question asked\n",
        "            answer: The response provided\n",
        "\n",
        "        Returns:\n",
        "            Dict containing analysis results\n",
        "        \"\"\"\n",
        "        # Identify the security domain\n",
        "        domain, domain_confidence = self._identify_domain(question, answer)\n",
        "\n",
        "        # Score the response\n",
        "        risk_score, risk_level, reasoning = self._analyze_response(domain, answer)\n",
        "\n",
        "        # Generate recommendations\n",
        "        recommendations = self._generate_recommendations(domain, risk_score, answer)\n",
        "\n",
        "        # Create response object\n",
        "        response = SecurityResponse(\n",
        "            question=question,\n",
        "            answer=answer,\n",
        "            domain=domain,\n",
        "            risk_score=risk_score,\n",
        "            risk_level=risk_level,\n",
        "            timestamp=datetime.now().isoformat(),\n",
        "            recommendations=recommendations,\n",
        "            reasoning=reasoning\n",
        "        )\n",
        "\n",
        "        # Update history and scores\n",
        "        self.conversation_history.append(response)\n",
        "        if domain not in self.risk_scores:\n",
        "            self.risk_scores[domain] = []\n",
        "        self.risk_scores[domain].append(risk_score)\n",
        "\n",
        "        # Generate next question\n",
        "        next_question = self._generate_follow_up(domain, answer, risk_score)\n",
        "\n",
        "        self.question_count[domain] += 1\n",
        "\n",
        "        # Change domain if the threshold is reached\n",
        "        if self.question_count[domain] >= 2:  # Change this threshold as needed\n",
        "            domain = self._change_domain(domain)  # Logic to switch to another domain\n",
        "            self.question_count[domain] = 0  # Reset counter after switching\n",
        "\n",
        "        return {\n",
        "            \"current_response\": response.__dict__,\n",
        "            \"next_question\": next_question,\n",
        "            \"domain_confidence\": domain_confidence,\n",
        "            \"risk_assessment\": self._calculate_risk_assessment()\n",
        "        }\n",
        "\n",
        "    def _identify_domain(self, question: str, answer: str) -> Tuple[str, float]:\n",
        "        \"\"\"\n",
        "        Identify the most relevant security domain for the response.\n",
        "        \"\"\"\n",
        "        combined_text = f\"{question} {answer}\"\n",
        "        text_embedding = self.encoder.encode([combined_text])[0]\n",
        "\n",
        "        best_match = None\n",
        "        best_score = -1\n",
        "\n",
        "        for domain_name, domain in self.security_domains.items():\n",
        "            domain_context = \" \".join(domain.keywords + domain.best_practices)\n",
        "            domain_embedding = self.encoder.encode([domain_context])[0]\n",
        "            similarity = np.dot(text_embedding, domain_embedding)\n",
        "\n",
        "            if similarity > best_score:\n",
        "                best_score = similarity\n",
        "                best_match = domain_name\n",
        "\n",
        "        return best_match, float(best_score)\n",
        "\n",
        "    def _analyze_response(self, domain: str, answer: str) -> Tuple[float, RiskLevel, str]:\n",
        "        \"\"\"\n",
        "        Analyze the response for risk scoring.\n",
        "        \"\"\"\n",
        "        domain_info = self.security_domains[domain]\n",
        "        answer_embedding = self.encoder.encode([answer])[0]\n",
        "\n",
        "        # Calculate similarity with best practices\n",
        "        best_practices_embedding = self.encoder.encode(domain_info.best_practices)\n",
        "        best_practices_similarities = np.dot(answer_embedding, best_practices_embedding.T)\n",
        "        best_practices_score = float(np.mean(best_practices_similarities))\n",
        "\n",
        "        # Calculate similarity with risk indicators\n",
        "        risk_indicators_embedding = self.encoder.encode(domain_info.risk_indicators)\n",
        "        risk_indicator_similarities = np.dot(answer_embedding, risk_indicators_embedding.T)\n",
        "\n",
        "        risk_indicator_weights = [1.5 if \"no\" in ind.lower() or \"lack\" in ind.lower() else 1.0\n",
        "                                for ind in domain_info.risk_indicators]\n",
        "        weighted_risk_score = float(np.dot(risk_indicator_similarities, risk_indicator_weights) / sum(risk_indicator_weights))\n",
        "\n",
        "        # Calculate overall risk score\n",
        "        risk_score = (best_practices_score + (1 - weighted_risk_score)) / 2\n",
        "\n",
        "        # Get domain-specific threshold\n",
        "        threshold = self.domain_risk_thresholds.get(domain, 0.7)\n",
        "\n",
        "        # Determine risk level\n",
        "        if risk_score >= threshold:\n",
        "            risk_level = RiskLevel.LOW\n",
        "        elif risk_score >= threshold - 0.2:\n",
        "            risk_level = RiskLevel.MEDIUM\n",
        "        elif risk_score >= threshold - 0.4:\n",
        "            risk_level = RiskLevel.HIGH\n",
        "        else:\n",
        "            risk_level = RiskLevel.CRITICAL\n",
        "\n",
        "        # Generate reasoning\n",
        "        reasoning = self._generate_reasoning(domain, best_practices_score, weighted_risk_score)\n",
        "\n",
        "        return risk_score, risk_level, reasoning\n",
        "\n",
        "    def _generate_recommendations(self, domain: str, risk_score: float, answer: str) -> List[str]:\n",
        "        \"\"\"\n",
        "        Generate recommendations based on the response analysis.\n",
        "        \"\"\"\n",
        "        domain_info = self.security_domains[domain]\n",
        "        answer_embedding = self.encoder.encode([answer])[0]\n",
        "\n",
        "        recommendations = []\n",
        "        practices_embeddings = self.encoder.encode(domain_info.best_practices)\n",
        "        similarities = np.dot(answer_embedding, practices_embeddings.T)\n",
        "\n",
        "        for i, similarity in enumerate(similarities):\n",
        "            if similarity < 0.5:\n",
        "                recommendations.append(f\"Consider implementing: {domain_info.best_practices[i]}\")\n",
        "\n",
        "        return recommendations\n",
        "\n",
        "    def _generate_follow_up(self, domain: str, answer: str, risk_score: float) -> str:\n",
        "        \"\"\"\n",
        "        Generate a follow-up question based on the context.\n",
        "        \"\"\"\n",
        "        domain_info = self.security_domains[domain]\n",
        "\n",
        "        if risk_score < 0.5:\n",
        "            templates = [t for t in domain_info.follow_up_templates\n",
        "                       if any(word in t.lower() for word in [\"how\", \"what measures\", \"plan\"])]\n",
        "        else:\n",
        "            templates = [t for t in domain_info.follow_up_templates\n",
        "                       if any(word in t.lower() for word in [\"monitor\", \"review\", \"audit\"])]\n",
        "\n",
        "        if not templates:\n",
        "            templates = domain_info.follow_up_templates\n",
        "\n",
        "        template = np.random.choice(templates)\n",
        "        return template.format(topic=domain_info.name.replace(\"_\", \" \"))\n",
        "\n",
        "    def _generate_reasoning(self, domain: str, best_practices_score: float, risk_indicator_score: float) -> str:\n",
        "        \"\"\"\n",
        "        Generate explanation for the risk assessment.\n",
        "        \"\"\"\n",
        "        return (f\"Domain: {domain}\\n\"\n",
        "                f\"Alignment with best practices: {best_practices_score:.2f}\\n\"\n",
        "                f\"Presence of risk indicators: {risk_indicator_score:.2f}\")\n",
        "\n",
        "    def _calculate_risk_assessment(self) -> Dict:\n",
        "        \"\"\"\n",
        "        Calculate overall risk assessment across all domains.\n",
        "        \"\"\"\n",
        "        assessment = {\n",
        "            \"overall_risk_score\": 0.0,\n",
        "            \"domain_scores\": {},\n",
        "            \"timestamp\": datetime.now().isoformat()\n",
        "        }\n",
        "\n",
        "        if not self.risk_scores:\n",
        "            return assessment\n",
        "\n",
        "        for domain, scores in self.risk_scores.items():\n",
        "            domain_score = sum(scores) / len(scores)\n",
        "            assessment[\"domain_scores\"][domain] = {\n",
        "                \"score\": domain_score,\n",
        "                \"risk_level\": (RiskLevel.LOW if domain_score >= 0.8\n",
        "                      else RiskLevel.MEDIUM if domain_score >= 0.6\n",
        "                      else RiskLevel.HIGH if domain_score >= 0.4\n",
        "                      else RiskLevel.CRITICAL).value\n",
        "            }\n",
        "            assessment[\"overall_risk_score\"] += domain_score\n",
        "\n",
        "        assessment[\"overall_risk_score\"] /= len(self.risk_scores)\n",
        "        return assessment\n",
        "\n",
        "    def generate_report(self) -> Dict:\n",
        "        \"\"\"\n",
        "        Generate comprehensive security assessment report.\n",
        "        \"\"\"\n",
        "        return {\n",
        "            \"assessment_summary\": self._calculate_risk_assessment(),\n",
        "            \"conversation_history\": [self._serialize_response(response) for response in self.conversation_history],\n",
        "            \"recommendations\": self._compile_overall_recommendations(),\n",
        "            \"timestamp\": datetime.now().isoformat()\n",
        "        }\n",
        "\n",
        "    def _serialize_response(self, response: SecurityResponse) -> Dict:\n",
        "        \"\"\"\n",
        "        Serialize SecurityResponse for JSON output.\n",
        "        \"\"\"\n",
        "        return {\n",
        "            \"question\": response.question,\n",
        "            \"answer\": response.answer,\n",
        "            \"domain\": response.domain,\n",
        "            \"risk_score\": response.risk_score,\n",
        "            \"risk_level\": response.risk_level.value,  # Convert enum to string\n",
        "            \"timestamp\": response.timestamp,\n",
        "            \"recommendations\": response.recommendations,\n",
        "            \"reasoning\": response.reasoning\n",
        "        }\n",
        "\n",
        "    def _compile_overall_recommendations(self) -> Dict[str, List[str]]:\n",
        "        \"\"\"\n",
        "        Compile recommendations by domain.\n",
        "        \"\"\"\n",
        "        domain_recommendations = {}\n",
        "        for response in self.conversation_history:\n",
        "            if response.domain not in domain_recommendations:\n",
        "                domain_recommendations[response.domain] = []\n",
        "            domain_recommendations[response.domain].extend(response.recommendations)\n",
        "\n",
        "        # Remove duplicates while preserving order\n",
        "        for domain in domain_recommendations:\n",
        "            domain_recommendations[domain] = list(dict.fromkeys(domain_recommendations[domain]))\n",
        "\n",
        "        return domain_recommendations\n",
        "\n",
        "\n",
        "    # def _change_domain(self, current_domain: str) -> str:\n",
        "    #     \"\"\"\n",
        "    #     Change the current domain based on recent conversations.\n",
        "    #     \"\"\"\n",
        "    #     current_domain = self.conversation_history[-1].domain\n",
        "    #     recent_domains = {response.domain for response in self.conversation_history[-self.question_count[current_domain]:]}\n",
        "\n",
        "    #     # Get all available domains\n",
        "    #     all_domains = list(self.security_domains.keys())\n",
        "\n",
        "    #     # Filter out the recent domains from the available domains\n",
        "    #     available_domains = [domain for domain in all_domains if domain not in recent_domains and domain != current_domain]\n",
        "\n",
        "    #     # If there are no available domains left, reset the recent domains tracking\n",
        "    #     if not available_domains:\n",
        "    #         available_domains = all_domains  # Reset to all domains if all have been discussed\n",
        "\n",
        "    #     # Select a new domain randomly from the available ones\n",
        "    #     new_domain = np.random.choice(available_domains)\n",
        "\n",
        "    #     return new_domain\n",
        "    def _change_domain(self, current_domain: str) -> str:\n",
        "        recent_domains = {response.domain for response in self.conversation_history[-self.question_count[current_domain]:]}\n",
        "        all_domains = list(self.security_domains.keys())\n",
        "        available_domains = [domain for domain in all_domains if domain not in recent_domains and domain != current_domain]\n",
        "\n",
        "        if not available_domains:\n",
        "            available_domains = all_domains  # Reset to all domains if all have been discussed\n",
        "\n",
        "        new_domain = np.random.choice(available_domains)\n",
        "        self.question_count[new_domain] = 0  # Reset the question count for the new domain\n",
        "\n",
        "        return new_domain\n"
      ],
      "metadata": {
        "id": "MY8hi-vZ9bgz"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from typing import List, Dict, Optional, Tuple\n",
        "import json\n",
        "from datetime import datetime\n",
        "from sentence_transformers import SentenceTransformer\n",
        "import numpy as np\n",
        "from enum import Enum\n",
        "import sys\n",
        "\n",
        "def get_valid_input(prompt: str, input_type: str = \"question\") -> str:\n",
        "    \"\"\"\n",
        "    Helper function to get valid input from user.\n",
        "    Keeps prompting until a non-empty response is provided.\n",
        "\n",
        "    Args:\n",
        "        prompt: The prompt to show to the user\n",
        "        input_type: Type of input (\"question\" or \"answer\") for customized messages\n",
        "\n",
        "    Returns:\n",
        "        str: Valid user input\n",
        "    \"\"\"\n",
        "    while True:\n",
        "        user_input = input(prompt).strip()\n",
        "\n",
        "        # Handle exit command\n",
        "        if user_input.lower() == 'exit':\n",
        "            return 'exit'\n",
        "\n",
        "        # Check for empty input\n",
        "        if not user_input:\n",
        "            if input_type == \"question\":\n",
        "                print(\"Question cannot be empty. Please enter a valid question.\")\n",
        "            else:\n",
        "                print(\"Response cannot be empty. Please provide a detailed answer.\")\n",
        "            continue\n",
        "\n",
        "        # Check for minimum length (optional)\n",
        "        if len(user_input.split()) < 2 and input_type == \"answer\":\n",
        "            print(\"Please provide a more detailed response (at least a few words).\")\n",
        "            continue\n",
        "\n",
        "        return user_input\n",
        "\n",
        "def run_security_assessment():\n",
        "    \"\"\"\n",
        "    Run an interactive security assessment conversation with improved error handling\n",
        "    and input validation.\n",
        "    \"\"\"\n",
        "    try:\n",
        "        # Initialize the conversation manager\n",
        "        manager = DynamicSecurityConversationManager()\n",
        "\n",
        "        # Print welcome message and instructions\n",
        "        print(\"\\n=== Security Assessment Conversation ===\")\n",
        "        print(\"---------------------------------------\")\n",
        "        print(\"Instructions:\")\n",
        "        print(\"- Provide detailed responses for better analysis\")\n",
        "        print(\"- Type 'exit' at any time to end the assessment and generate report\")\n",
        "        print(\"- Empty responses are not accepted\")\n",
        "        print(\"---------------------------------------\\n\")\n",
        "\n",
        "        question_count = 1\n",
        "        next_question = \"What is the compliance protocol you follow?\"\n",
        "\n",
        "        while True:\n",
        "            try:\n",
        "                # Display the current question\n",
        "                print(f\"\\nQuestion #{question_count}\")\n",
        "                print(f\"Current Question: {next_question}\")\n",
        "\n",
        "                # Get the response with validation\n",
        "                answer = get_valid_input(\"Your Response: \", \"answer\")\n",
        "\n",
        "                if answer.lower() == 'exit':\n",
        "                    break\n",
        "\n",
        "                # Process the response\n",
        "                result = manager.process_response(next_question, answer)\n",
        "\n",
        "                # Print analysis with improved formatting\n",
        "                print(\"\\n=== Analysis ===\")\n",
        "                print(f\"Domain: {result['current_response']['domain']}\")\n",
        "                print(f\"Confidence: {result['domain_confidence']:.2f}\")\n",
        "                print(f\"Risk Level: {result['current_response']['risk_level']}\")\n",
        "                print(f\"Risk Score: {result['current_response']['risk_score']:.2f}\")\n",
        "\n",
        "                # Print recommendations if any\n",
        "                if result['current_response']['recommendations']:\n",
        "                    print(\"\\n=== Recommendations ===\")\n",
        "                    for i, rec in enumerate(result['current_response']['recommendations'], 1):\n",
        "                        print(f\"{i}. {rec}\")\n",
        "\n",
        "                # Print reasoning\n",
        "                print(\"\\n=== Reasoning ===\")\n",
        "                print(result['current_response']['reasoning'])\n",
        "\n",
        "                # Get the next question for the next iteration\n",
        "                if result['next_question']:\n",
        "                    next_question = result['next_question']\n",
        "                    print(\"\\n=== Next Question ===\")\n",
        "                    print(next_question)\n",
        "                else:\n",
        "                    # Fallback question if no next question is generated\n",
        "                    next_question = \"Can you describe any additional security measures or concerns?\"\n",
        "\n",
        "                print(\"\\n\" + \"=\"*50)\n",
        "\n",
        "                question_count += 1\n",
        "\n",
        "            except Exception as e:\n",
        "                print(f\"\\nError processing response: {str(e)}\")\n",
        "                print(\"Please try again.\")\n",
        "                continue\n",
        "\n",
        "        # Generate and save final report\n",
        "        if question_count > 1:  # Only generate report if there were questions\n",
        "            try:\n",
        "                final_report = manager.generate_report()\n",
        "\n",
        "                # Save report with timestamp\n",
        "                timestamp = datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n",
        "                report_filename = f'security_assessment_report_{timestamp}.json'\n",
        "\n",
        "                with open(report_filename, 'w') as f:\n",
        "                    json.dump(final_report, f, indent=4)\n",
        "\n",
        "                print(f\"\\nFinal Report generated and saved to '{report_filename}'\")\n",
        "\n",
        "                # Print summary\n",
        "                print(\"\\n=== Summary of Findings ===\")\n",
        "                print(f\"Overall Risk Score: {final_report['assessment_summary']['overall_risk_score']:.2f}\")\n",
        "                print(\"\\nDomain Risk Levels:\")\n",
        "                print(\"-\" * 30)\n",
        "                for domain, scores in final_report['assessment_summary']['domain_scores'].items():\n",
        "                    risk_level = scores['risk_level']\n",
        "                    risk_score = scores['score']\n",
        "                    print(f\"{domain:20}: {risk_level:8} ({risk_score:.2f})\")\n",
        "\n",
        "                # Print key recommendations\n",
        "                print(\"\\nKey Recommendations by Domain:\")\n",
        "                print(\"-\" * 30)\n",
        "                for domain, recs in final_report['recommendations'].items():\n",
        "                    if recs:  # Only print domains with recommendations\n",
        "                        print(f\"\\n{domain}:\")\n",
        "                        for i, rec in enumerate(recs[:3], 1):  # Show top 3 recommendations per domain\n",
        "                            print(f\"{i}. {rec}\")\n",
        "\n",
        "            except Exception as e:\n",
        "                print(f\"\\nError generating report: {str(e)}\")\n",
        "                print(\"Please check the application logs for more details.\")\n",
        "\n",
        "        else:\n",
        "            print(\"\\nNo assessment data collected. Report generation skipped.\")\n",
        "\n",
        "    except KeyboardInterrupt:\n",
        "        print(\"\\n\\nAssessment interrupted by user.\")\n",
        "        sys.exit(0)\n",
        "\n",
        "    except Exception as e:\n",
        "        print(f\"\\nCritical error: {str(e)}\")\n",
        "        print(\"The assessment has been terminated due to an unexpected error.\")\n",
        "        sys.exit(1)\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "    run_security_assessment()"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 1000,
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            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
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            "\n",
            "=== Security Assessment Conversation ===\n",
            "---------------------------------------\n",
            "Instructions:\n",
            "- Provide detailed responses for better analysis\n",
            "- Type 'exit' at any time to end the assessment and generate report\n",
            "- Empty responses are not accepted\n",
            "---------------------------------------\n",
            "\n",
            "\n",
            "Question #1\n",
            "Current Question: What is the compliance protocol you follow?\n",
            "Your Response: we follow GDPR protocols\n",
            "\n",
            "=== Analysis ===\n",
            "Domain: compliance\n",
            "Confidence: 0.50\n",
            "Risk Level: RiskLevel.MEDIUM\n",
            "Risk Score: 0.55\n",
            "\n",
            "=== Recommendations ===\n",
            "1. Consider implementing: regular audits\n",
            "2. Consider implementing: documentation maintenance\n",
            "3. Consider implementing: staff training\n",
            "4. Consider implementing: incident response plan\n",
            "5. Consider implementing: compliance monitoring\n",
            "\n",
            "=== Reasoning ===\n",
            "Domain: compliance\n",
            "Alignment with best practices: 0.22\n",
            "Presence of risk indicators: 0.13\n",
            "\n",
            "=== Next Question ===\n",
            "What documentation exists for compliance?\n",
            "\n",
            "==================================================\n",
            "\n",
            "Question #2\n",
            "Current Question: What documentation exists for compliance?\n",
            "Your Response: we have included ISO 270001, GDPR and HIPPA\n",
            "\n",
            "=== Analysis ===\n",
            "Domain: compliance\n",
            "Confidence: 0.74\n",
            "Risk Level: RiskLevel.MEDIUM\n",
            "Risk Score: 0.57\n",
            "\n",
            "=== Recommendations ===\n",
            "1. Consider implementing: regular audits\n",
            "2. Consider implementing: documentation maintenance\n",
            "3. Consider implementing: staff training\n",
            "4. Consider implementing: incident response plan\n",
            "5. Consider implementing: compliance monitoring\n",
            "\n",
            "=== Reasoning ===\n",
            "Domain: compliance\n",
            "Alignment with best practices: 0.28\n",
            "Presence of risk indicators: 0.15\n",
            "\n",
            "=== Next Question ===\n",
            "What training is provided for compliance?\n",
            "\n",
            "==================================================\n",
            "\n",
            "Question #3\n",
            "Current Question: What training is provided for compliance?\n",
            "Your Response: regular audits and phishing training\n",
            "\n",
            "=== Analysis ===\n",
            "Domain: compliance\n",
            "Confidence: 0.64\n",
            "Risk Level: RiskLevel.MEDIUM\n",
            "Risk Score: 0.61\n",
            "\n",
            "=== Recommendations ===\n",
            "1. Consider implementing: documentation maintenance\n",
            "2. Consider implementing: staff training\n",
            "3. Consider implementing: incident response plan\n",
            "4. Consider implementing: compliance monitoring\n",
            "\n",
            "=== Reasoning ===\n",
            "Domain: compliance\n",
            "Alignment with best practices: 0.39\n",
            "Presence of risk indicators: 0.17\n",
            "\n",
            "=== Next Question ===\n",
            "What training is provided for compliance?\n",
            "\n",
            "==================================================\n",
            "\n",
            "Question #4\n",
            "Current Question: What training is provided for compliance?\n",
            "Your Response: no training is provided\n",
            "\n",
            "=== Analysis ===\n",
            "Domain: compliance\n",
            "Confidence: 0.53\n",
            "Risk Level: RiskLevel.HIGH\n",
            "Risk Score: 0.42\n",
            "\n",
            "=== Recommendations ===\n",
            "1. Consider implementing: regular audits\n",
            "2. Consider implementing: documentation maintenance\n",
            "3. Consider implementing: incident response plan\n",
            "4. Consider implementing: compliance monitoring\n",
            "\n",
            "=== Reasoning ===\n",
            "Domain: compliance\n",
            "Alignment with best practices: 0.20\n",
            "Presence of risk indicators: 0.36\n",
            "\n",
            "=== Next Question ===\n",
            "How do you ensure ongoing compliance compliance?\n",
            "\n",
            "==================================================\n",
            "\n",
            "Question #5\n",
            "Current Question: How do you ensure ongoing compliance compliance?\n",
            "Your Response: Regular Audits, Compliance with Data Privacy Regulations\n",
            "\n",
            "=== Analysis ===\n",
            "Domain: compliance\n",
            "Confidence: 0.66\n",
            "Risk Level: RiskLevel.MEDIUM\n",
            "Risk Score: 0.61\n",
            "\n",
            "=== Recommendations ===\n",
            "1. Consider implementing: documentation maintenance\n",
            "2. Consider implementing: staff training\n",
            "3. Consider implementing: incident response plan\n",
            "\n",
            "=== Reasoning ===\n",
            "Domain: compliance\n",
            "Alignment with best practices: 0.38\n",
            "Presence of risk indicators: 0.17\n",
            "\n",
            "=== Next Question ===\n",
            "What training is provided for compliance?\n",
            "\n",
            "==================================================\n",
            "\n",
            "Question #6\n",
            "Current Question: What training is provided for compliance?\n",
            "Your Response: Regular Sessions, Incident Response Plan, IRP, GDPR\n",
            "\n",
            "=== Analysis ===\n",
            "Domain: compliance\n",
            "Confidence: 0.71\n",
            "Risk Level: RiskLevel.MEDIUM\n",
            "Risk Score: 0.61\n",
            "\n",
            "=== Recommendations ===\n",
            "1. Consider implementing: regular audits\n",
            "2. Consider implementing: documentation maintenance\n",
            "3. Consider implementing: staff training\n",
            "4. Consider implementing: compliance monitoring\n",
            "\n",
            "=== Reasoning ===\n",
            "Domain: compliance\n",
            "Alignment with best practices: 0.43\n",
            "Presence of risk indicators: 0.22\n",
            "\n",
            "=== Next Question ===\n",
            "What documentation exists for compliance?\n",
            "\n",
            "==================================================\n",
            "\n",
            "Question #7\n",
            "Current Question: What documentation exists for compliance?\n",
            "Your Response: Regulations Documentation like GDPR\n",
            "\n",
            "=== Analysis ===\n",
            "Domain: compliance\n",
            "Confidence: 0.66\n",
            "Risk Level: RiskLevel.MEDIUM\n",
            "Risk Score: 0.54\n",
            "\n",
            "=== Recommendations ===\n",
            "1. Consider implementing: regular audits\n",
            "2. Consider implementing: documentation maintenance\n",
            "3. Consider implementing: staff training\n",
            "4. Consider implementing: incident response plan\n",
            "5. Consider implementing: compliance monitoring\n",
            "\n",
            "=== Reasoning ===\n",
            "Domain: compliance\n",
            "Alignment with best practices: 0.29\n",
            "Presence of risk indicators: 0.20\n",
            "\n",
            "=== Next Question ===\n",
            "What documentation exists for compliance?\n",
            "\n",
            "==================================================\n",
            "\n",
            "Question #8\n",
            "Current Question: What documentation exists for compliance?\n",
            "Your Response: HIPPA\n",
            "Please provide a more detailed response (at least a few words).\n",
            "Your Response: HIPPA guidelines for compliance\n",
            "\n",
            "=== Analysis ===\n",
            "Domain: compliance\n",
            "Confidence: 0.60\n",
            "Risk Level: RiskLevel.MEDIUM\n",
            "Risk Score: 0.56\n",
            "\n",
            "=== Recommendations ===\n",
            "1. Consider implementing: regular audits\n",
            "2. Consider implementing: documentation maintenance\n",
            "3. Consider implementing: staff training\n",
            "4. Consider implementing: incident response plan\n",
            "5. Consider implementing: compliance monitoring\n",
            "\n",
            "=== Reasoning ===\n",
            "Domain: compliance\n",
            "Alignment with best practices: 0.26\n",
            "Presence of risk indicators: 0.14\n",
            "\n",
            "=== Next Question ===\n",
            "What documentation exists for compliance?\n",
            "\n",
            "==================================================\n",
            "\n",
            "Question #9\n",
            "Current Question: What documentation exists for compliance?\n",
            "Your Response: exit\n",
            "\n",
            "Final Report generated and saved to 'security_assessment_report_20241020_073633.json'\n",
            "\n",
            "=== Summary of Findings ===\n",
            "Overall Risk Score: 0.56\n",
            "\n",
            "Domain Risk Levels:\n",
            "------------------------------\n",
            "compliance          : High     (0.56)\n",
            "\n",
            "Key Recommendations by Domain:\n",
            "------------------------------\n",
            "\n",
            "compliance:\n",
            "1. Consider implementing: regular audits\n",
            "2. Consider implementing: documentation maintenance\n",
            "3. Consider implementing: staff training\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "from openpyxl import Workbook\n",
        "from openpyxl.styles import PatternFill\n",
        "\n",
        "# Load the JSON data from final.json\n",
        "with open('/content/security_assessment_report_20241020_050008.json', 'r') as json_file:\n",
        "    data = json.load(json_file)\n",
        "\n",
        "wb = Workbook()\n",
        "ws = wb.active\n",
        "ws.title = \"Risk Assessment Report\"\n",
        "\n",
        "# Define headers\n",
        "headers = ['Question', 'Answer', 'Risk Level', 'Risk Score', 'Reasoning', 'Recommendations']\n",
        "ws.append(headers)\n",
        "\n",
        "# Define color fills for risk levels\n",
        "fill_red = PatternFill(start_color=\"FFCCCC\", end_color=\"FFCCCC\", fill_type=\"solid\")  # High risk\n",
        "fill_orange = PatternFill(start_color=\"FFEBB5\", end_color=\"FFEBB5\", fill_type=\"solid\")  # Medium risk\n",
        "fill_green = PatternFill(start_color=\"D5E8D4\", end_color=\"D5E8D4\", fill_type=\"solid\")  # Low risk\n",
        "\n",
        "# Iterate through the conversation history in final.json\n",
        "for entry in data['conversation_history']:\n",
        "    row = [\n",
        "        entry['question'],\n",
        "        entry['answer'],\n",
        "        entry['risk_level'],\n",
        "        f\"{entry['risk_score']:.2f}\",\n",
        "        entry['reasoning'],\n",
        "        ', '.join(entry['recommendations'])\n",
        "    ]\n",
        "    ws.append(row)\n",
        "\n",
        "    # Apply the fill color based on risk level\n",
        "    last_row = ws.max_row  # Get the last row index\n",
        "    if entry['risk_level'] == 'High':\n",
        "        for cell in ws[last_row]:\n",
        "            cell.fill = fill_red\n",
        "    elif entry['risk_level'] == 'Medium':\n",
        "        for cell in ws[last_row]:\n",
        "            cell.fill = fill_orange\n",
        "    elif entry['risk_level'] == 'Low':\n",
        "        for cell in ws[last_row]:\n",
        "            cell.fill = fill_green\n",
        "\n",
        "# Save the workbook to a file\n",
        "excel_file_name = \"risk_assessment_report.xlsx\"\n",
        "wb.save(excel_file_name)\n",
        "print(f\"Excel report generated: {excel_file_name}\")"
      ],
      "metadata": {
        "id": "_aA-WeP396aB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "af123d2f-c6af-4810-ace0-c69fa772f9e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Excel report generated: risk_assessment_report.xlsx\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "oYDK3zm1PRiz"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}