Feature: Implement LLM-driven code audit summary generation

backend/app/services/nlg/nlg_engine.py

@@ -127,3 +127,38 @@ async def generate_sentiment_text(self, raw_data: Dict[str, Any]) -> str:
             error_msg="Failed to generate social sentiment summary due to an internal error. Please try again later."
         )
 
+    async def generate_code_audit_text(self, code_data: Dict[str, Any], audit_data: Dict[str, Any]) -> str:
+        """
+        Generates a comprehensive code audit summary using LLM prompts.
+        Includes clarity points, risk highlights, code activity, and repository quality indicators.
+        Handles missing audit information gracefully.
+        """
+        if not code_data and not audit_data:
+            return self._format_output({
+                "section_id": "code_audit_summary",
+                "text": "Code audit and repository data are not available at this time. Please check back later for updates."
+            })
+
+        # Combine data for the prompt, handling potentially missing parts
+        combined_data = {
+            "code_data": json.dumps(code_data, indent=2) if code_data else "N/A",
+            "audit_data": json.dumps(audit_data, indent=2) if audit_data else "N/A",
+        }
+
+        template = get_template("code_audit_summary")
+        prompt = fill_template(template, **combined_data)
+
+        async with LLMClient() as llm_client:
+            try:
+                response = await llm_client.generate_text(prompt)
+                generated_text = response.get("choices", [{}])[0].get("message", {}).get("content", "").strip()
+                if not generated_text:
+                    raise ValueError("LLM returned empty content for code_audit_summary.")
+                return self._format_output({"section_id": "code_audit_summary", "text": generated_text})
+            except Exception as e:
+                logger.error(f"Error generating code_audit_summary text with LLM: {e}", exc_info=True)
+                return self._format_output({
+                    "section_id": "code_audit_summary",
+                    "text": "Failed to generate code audit summary due to an internal error. Please try again later."
+                })
+

backend/app/services/nlg/prompt_templates.py

@@ -67,6 +67,27 @@ def get_template(section_id: str) -> str:
         Risk Factors Data:
         {data}
         """,
+        "code_audit_summary": """
+        Based on the provided code audit and repository data, generate a comprehensive audit summary.
+        The summary should include:
+        1.  **Clarity Points**: Highlight aspects of the codebase that are well-structured,
+            easy to understand, and follow best practices.
+        2.  **Risk Highlights**: Identify potential security vulnerabilities, performance bottlenecks,
+            or maintainability issues.
+        3.  **Code Activity**: Summarize recent development activity, such as commit frequency,
+            contributor engagement, and major feature implementations.
+        4.  **Repository Quality Indicators**: Comment on aspects like test coverage,
+            documentation quality, and adherence to coding standards.
+
+        Handle cases where specific audit information might be missing by stating
+        that the information is not available or could not be assessed.
+
+        Code Audit Data:
+        {code_data}
+
+        Audit Data:
+        {audit_data}
+        """,
         "team_roles_summary": """
         Based on the following team member data, summarize the key roles and responsibilities
         identified within the team. Highlight the diversity of roles and how they contribute

backend/app/services/nlg/tests/test_nlg_engine.py

@@ -173,23 +173,72 @@ async def test_generate_sentiment_text_llm_error():
         assert "Failed to generate social sentiment summary due to an internal error." in parsed_response["text"]
         assert respx_mock.calls.call_count == 1
 
+        assert parsed_response["section_id"] == "social_sentiment"
+        assert "Failed to generate social sentiment summary due to an internal error." in parsed_response["text"]
+        assert respx_mock.calls.call_count == 1
+
 @pytest.mark.asyncio
-async def test_generate_sentiment_text_llm_empty_content():
+async def test_generate_code_audit_text_success():
     engine = ConcreteNLGEngine()
-    raw_data = {
-        "overall_sentiment_score": 0.75,
-        "community_perception": "positive"
+    code_data = {"files": [{"name": "main.py", "lines": 100}]}
+    audit_data = {"risks": [{"type": "security", "severity": "high"}]}
+    expected_llm_response = {
+        "choices": [{"message": {"content": "Code audit summary: Clarity is good. High security risk identified. Recent code activity. Good repository quality."}}]
     }
+
+    with respx.mock as respx_mock:
+        respx_mock.post("https://api.openai.com/v1/chat/completions").return_value = Response(200, json=expected_llm_response)
+
+        response = await engine.generate_code_audit_text(code_data, audit_data)
+        parsed_response = json.loads(response)
+
+        assert parsed_response["section_id"] == "code_audit_summary"
+        assert "Code audit summary: Clarity is good." in parsed_response["text"]
+        assert respx_mock.calls.call_count == 1
+
+@pytest.mark.asyncio
+async def test_generate_code_audit_text_empty_data():
+    engine = ConcreteNLGEngine()
+    code_data = {}
+    audit_data = {}
+
+    response = await engine.generate_code_audit_text(code_data, audit_data)
+    parsed_response = json.loads(response)
+
+    assert parsed_response["section_id"] == "code_audit_summary"
+    assert "Code audit and repository data are not available at this time." in parsed_response["text"]
+
+@pytest.mark.asyncio
+async def test_generate_code_audit_text_llm_error():
+    engine = ConcreteNLGEngine()
+    code_data = {"files": [{"name": "main.py", "lines": 100}]}
+    audit_data = {"risks": [{"type": "security", "severity": "high"}]}
+
+    with respx.mock as respx_mock:
+        respx_mock.post("https://api.openai.com/v1/chat/completions").return_value = Response(500, text="Internal Server Error")
+
+        response = await engine.generate_code_audit_text(code_data, audit_data)
+        parsed_response = json.loads(response)
+
+        assert parsed_response["section_id"] == "code_audit_summary"
+        assert "Failed to generate code audit summary due to an internal error." in parsed_response["text"]
+        assert respx_mock.calls.call_count == 1
+
+@pytest.mark.asyncio
+async def test_generate_code_audit_text_llm_empty_content():
+    engine = ConcreteNLGEngine()
+    code_data = {"files": [{"name": "main.py", "lines": 100}]}
+    audit_data = {"risks": [{"type": "security", "severity": "high"}]}
     expected_llm_response = {
         "choices": [{"message": {"content": ""}}]
     }
 
     with respx.mock as respx_mock:
         respx_mock.post("https://api.openai.com/v1/chat/completions").return_value = Response(200, json=expected_llm_response)
 
-        response = await engine.generate_sentiment_text(raw_data)
+        response = await engine.generate_code_audit_text(code_data, audit_data)
         parsed_response = json.loads(response)
 
-        assert parsed_response["section_id"] == "social_sentiment"
-        assert "Failed to generate social sentiment summary due to an internal error." in parsed_response["text"]
+        assert parsed_response["section_id"] == "code_audit_summary"
+        assert "Failed to generate code audit summary due to an internal error." in parsed_response["text"]
         assert respx_mock.calls.call_count == 1