Upload notebooks/04_agent_intelligence.ipynb with huggingface_hub

notebooks/04_agent_intelligence.ipynb

@@ -40,7 +40,9 @@
     "warnings.filterwarnings('ignore')\n",
     "\n",
     "# Load configuration\n",
-    "config_path = Path(\"notebook_config.json\")\nif not config_path.exists():\n    config_path = Path(\"/home/user/app/notebooks/notebook_config.json\")\n",
+    "config_path = Path(\"notebook_config.json\")\n",
+    "if not config_path.exists():\n",
+    "    config_path = Path(\"/home/user/app/notebooks/notebook_config.json\")\n",
     "with open(config_path) as f:\n",
     "    CONFIG = json.load(f)\n",
     "\n",
@@ -48,8 +50,8 @@
     "AGENT_DIR = MODELS_DIR.parent / \"agent\"\n",
     "AGENT_DIR.mkdir(exist_ok=True)\n",
     "\n",
-    "print(f\"\u2713 Configuration loaded\")\n",
-    "print(f\"\u2713 Agent output: {AGENT_DIR}\")"
+    "print(f\"✓ Configuration loaded\")\n",
+    "print(f\"✓ Agent output: {AGENT_DIR}\")"
    ]
   },
   {
@@ -103,7 +105,7 @@
     "    def to_dict(self) -> Dict:\n",
     "        return asdict(self)\n",
     "\n",
-    "print(\"\u2713 Task definitions loaded\")"
+    "print(\"✓ Task definitions loaded\")"
    ]
   },
   {
@@ -207,7 +209,7 @@
     "                      reverse=True)\n",
     "\n",
     "scoring_engine = DecisionScoringEngine()\n",
-    "print(\"\u2713 Decision Scoring Engine initialized\")"
+    "print(\"✓ Decision Scoring Engine initialized\")"
    ]
   },
   {
@@ -225,13 +227,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "# Gemini Integration - using google-genai (same as ml-services/app/services/gemini_service.py)\n",
     "try:\n",
-    "    import google.generativeai as genai\n",
+    "    from google import genai\n",
     "    GEMINI_AVAILABLE = True\n",
     "except ImportError:\n",
     "    import subprocess\n",
-    "    subprocess.run(['pip', 'install', 'google-generativeai', '-q'])\n",
-    "    import google.generativeai as genai\n",
+    "    subprocess.run(['pip', 'install', 'google-genai', '-q'])\n",
+    "    from google import genai\n",
     "    GEMINI_AVAILABLE = True\n",
     "\n",
     "class GeminiReasoningEngine:\n",
@@ -257,21 +260,30 @@
     "    \n",
     "    def __init__(self):\n",
     "        self.api_key = CONFIG.get('gemini_api_key', os.environ.get('GEMINI_API_KEY'))\n",
-    "        self.model = None\n",
+    "        self.client = None\n",
+    "        self.model_name = CONFIG.get('gemini_model', os.environ.get('GEMINI_MODEL', 'gemini-2.5-flash'))\n",
     "        \n",
     "        if self.api_key:\n",
     "            try:\n",
-    "                genai.configure(api_key=self.api_key)\n",
-    "                self.model = genai.GenerativeModel(CONFIG.get('gemini_model', 'gemini-pro'))\n",
-    "                print(\"  \u2713 Gemini API connected\")\n",
+    "                self.client = genai.Client(api_key=self.api_key)\n",
+    "                # Test connection\n",
+    "                test = self.client.models.generate_content(\n",
+    "                    model=self.model_name,\n",
+    "                    contents=\"Test. Respond with OK.\"\n",
+    "                )\n",
+    "                if test.text:\n",
+    "                    print(f\"  ✓ Gemini API connected (model: {self.model_name})\")\n",
+    "                else:\n",
+    "                    print(\"  ⚠ Gemini API responded but with empty text\")\n",
     "            except Exception as e:\n",
-    "                print(f\"  \u26a0 Gemini API error: {e}\")\n",
+    "                print(f\"  ⚠ Gemini API error: {e}\")\n",
+    "                self.client = None\n",
     "        else:\n",
-    "            print(\"  \u26a0 No Gemini API key found (will use fallback reasoning)\")\n",
+    "            print(\"  ⚠ No Gemini API key found (will use fallback reasoning)\")\n",
     "    \n",
     "    def analyze_threat(self, threat_data: Dict) -> AgentDecision:\n",
     "        \"\"\"Analyze threat and generate decision with reasoning\"\"\"\n",
-    "        if self.model:\n",
+    "        if self.client:\n",
     "            return self._gemini_analyze(threat_data)\n",
     "        else:\n",
     "            return self._fallback_analyze(threat_data)\n",
@@ -286,7 +298,14 @@
     "Provide your analysis as JSON.\"\"\"\n",
     "        \n",
     "        try:\n",
-    "            response = self.model.generate_content(prompt)\n",
+    "            response = self.client.models.generate_content(\n",
+    "                model=self.model_name,\n",
+    "                contents=prompt,\n",
+    "                config={\n",
+    "                    \"temperature\": 0.3,\n",
+    "                    \"max_output_tokens\": 1024,\n",
+    "                }\n",
+    "            )\n",
     "            \n",
     "            # Parse response\n",
     "            text = response.text\n",
@@ -307,7 +326,7 @@
     "                recommended_follow_up=result.get('recommended_follow_up', [])\n",
     "            )\n",
     "        except Exception as e:\n",
-    "            print(f\"  \u26a0 Gemini error: {e}\")\n",
+    "            print(f\"  ⚠ Gemini error: {e}\")\n",
     "            return self._fallback_analyze(threat_data)\n",
     "    \n",
     "    def _fallback_analyze(self, threat_data: Dict) -> AgentDecision:\n",
@@ -339,7 +358,7 @@
     "        )\n",
     "\n",
     "reasoning_engine = GeminiReasoningEngine()\n",
-    "print(\"\u2713 Gemini Reasoning Engine initialized\")"
+    "print(\"✓ Gemini Reasoning Engine initialized\")"
    ]
   },
   {
@@ -435,7 +454,7 @@
     "        }\n",
     "\n",
     "task_queue = AgentTaskQueue()\n",
-    "print(\"\u2713 Task Queue Manager initialized\")"
+    "print(\"✓ Task Queue Manager initialized\")"
    ]
   },
   {
@@ -560,7 +579,7 @@
     "        }\n",
     "\n",
     "agent = CyberForgeAgent()\n",
-    "print(\"\u2713 CyberForge Agent initialized\")"
+    "print(\"✓ CyberForge Agent initialized\")"
    ]
   },
   {
@@ -634,14 +653,14 @@
     "    'severity_weights': DecisionScoringEngine.SEVERITY_WEIGHTS,\n",
     "    'evidence_weights': DecisionScoringEngine.EVIDENCE_WEIGHTS,\n",
     "    'task_priorities': {p.name: p.value for p in TaskPriority},\n",
-    "    'gemini_model': 'gemini-pro'\n",
+    "    'gemini_model': CONFIG.get('gemini_model', 'gemini-2.5-flash')\n",
     "}\n",
     "\n",
     "config_path = AGENT_DIR / \"agent_config.json\"\n",
     "with open(config_path, 'w') as f:\n",
     "    json.dump(agent_config, f, indent=2)\n",
     "\n",
-    "print(f\"\u2713 Agent config saved to: {config_path}\")"
+    "print(f\"✓ Agent config saved to: {config_path}\")"
    ]
   },
   {
@@ -718,7 +737,7 @@
     "with open(module_path, 'w') as f:\n",
     "    f.write(agent_module)\n",
     "\n",
-    "print(f\"\u2713 Agent module saved to: {module_path}\")"
+    "print(f\"✓ Agent module saved to: {module_path}\")"
    ]
   },
   {
@@ -741,23 +760,23 @@
     "print(\"=\" * 60)\n",
     "\n",
     "print(f\"\"\"\n",
-    "\ud83e\udd16 Agent Capabilities:\n",
+    "🤖 Agent Capabilities:\n",
     "   - Decision Scoring: Weighted threat assessment\n",
     "   - Gemini Integration: AI-powered reasoning\n",
     "   - Task Queue: Priority-based execution\n",
     "   - Action History: Full audit trail\n",
     "\n",
-    "\ud83d\udcca Test Results:\n",
+    "📊 Test Results:\n",
     "   - Action: {decision.action}\n",
     "   - Confidence: {decision.confidence:.2%}\n",
     "   - Risk Level: {decision.risk_level}\n",
     "\n",
-    "\ud83d\udcc1 Output Files:\n",
+    "📁 Output Files:\n",
     "   - Config: {AGENT_DIR}/agent_config.json\n",
     "   - Module: {AGENT_DIR}/cyberforge_agent.py\n",
     "\n",
     "Next step:\n",
-    "  \u2192 05_model_validation.ipynb\n",
+    "  → 05_model_validation.ipynb\n",
     "\"\"\")\n",
     "print(\"=\" * 60)"
    ]
@@ -770,4 +789,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}