EX-329: added jira comment integration v1

Author: MUlanovych

src/code_review_agent/cli.py

@@ -11,6 +11,7 @@
 from . import bitbucket_client, github_client
 from . import git_utils, context_builder, reviewer
 from . import jira_client
+from . import relevance_assessor
 from .models import IssueType, CodeIssue
 
 def prioritize_changed_files(changed_files_map: Dict[str, str]) -> List[List[str]]:
@@ -328,6 +329,27 @@ def review(
                 if issue.suggestion:
                     logging.info(f"    💡 Suggestion: {issue.suggestion}")
                     logging.info(f"    ```\n    {issue.suggestion}\n    ```")
+                    
+    if task_id:
+        logging.info("\n---  Assessing Task Relevance ---")
+        
+        review_summary = f"Found {len(all_issues)} issue(s)."
+        
+        relevance = relevance_assessor.assess_relevance(
+            jira_details=jira_details_text,
+            commit_messages=commit_messages,
+            diff_text="\n".join(changed_files_map.values()),
+            review_summary=review_summary,
+            llm_config=llm_config
+        )
+
+        if relevance:
+            comment_body = (
+                f"🤖 **AI Assessment Complete for this PR**\n\n"
+                f"/!\\ The code changes have a **{relevance.score}%** relevance score to this task.\n\n"
+                f"**Justification:** {relevance.justification}"
+            )
+            jira_client.add_comment(task_id, comment_body)                    
 
 
     if not all_issues:

src/code_review_agent/jira_client.py

@@ -45,4 +45,17 @@ def get_task_details(task_id: str) -> dict | None:
         }
     except Exception as e:
         logger.warning(f"Could not fetch details for Jira task {task_id}. Error: {e}")
-        return None
+        return None
+    
+def add_comment(task_id: str, comment: str):
+    """Adds a comment to a Jira issue."""
+    client = _get_jira_client()
+    if not client or not task_id:
+        return
+    
+    logger.info(f"Adding comment to Jira task {task_id}...")
+    try:
+        client.add_comment(task_id, comment)
+        logger.info("✅ Successfully added comment to Jira.")
+    except Exception as e:
+        logger.error(f"Failed to add comment to Jira task {task_id}. Error: {e}")    

src/code_review_agent/models.py

@@ -33,3 +33,16 @@ class ReviewResult(BaseModel):
 
     def is_ok(self) -> bool:
         return not self.issues
+
+
+class TaskRelevance(BaseModel):
+    score: int = Field(
+        ..., 
+        description="A score from 0 to 100 representing how related the code change is to the Jira task.",
+        ge=0,
+        le=100 
+    )
+    justification: str = Field(
+        ...,
+        description="A brief, one-sentence justification for the score."
+    )

src/code_review_agent/relevance_assessor.py

@@ -0,0 +1,73 @@
+import json
+import logging
+from pydantic import ValidationError
+from .models import TaskRelevance
+from .llm_client import get_client
+
+logger = logging.getLogger(__name__)
+
+def assess_relevance(
+    jira_details: str,
+    commit_messages: str,
+    diff_text: str,
+    review_summary: str,
+    llm_config: dict,
+) -> TaskRelevance | None:
+    
+    client = get_client(llm_config)
+    
+    model = llm_config.get('models', {}).get('assessor', 'google/gemini-flash-1.5')
+
+    system_prompt = """
+    You are an expert project manager AI. Your task is to assess how relevant a code change is to its associated Jira task.
+    You MUST provide a score from 0 to 100 and a brief justification.
+
+    CRITICAL OUTPUT FORMATTING RULE:
+    Your entire response MUST be a single, valid JSON object with two keys: "score" (integer, 0-100) and "justification" (string).
+    Do not add any other text or explanations.
+    """
+
+    user_prompt = f"""
+    Please assess the relevance of the following code changes to the Jira task.
+
+    {jira_details}
+
+    **Commit Messages:**
+    ```
+    {commit_messages}
+    ```
+
+    **Summary of Code Changes (Git Diff):**
+    ```diff
+    {diff_text}
+    ```
+
+    **AI Code Review Summary:**
+    ```
+    {review_summary}
+    ```
+
+    Based on all this information, rate from 0 to 100% how related the code change is to the Jira task.
+    Return your assessment as a raw JSON object string.
+    """
+    
+    try:
+        response = client.chat.completions.create(
+            model=model,
+            messages=[
+                {"role": "system", "content": system_prompt},
+                {"role": "user", "content": user_prompt},
+            ]
+        )
+        raw_response_text = response.choices.message.content.strip()
+        
+        try:
+            parsed_json = json.loads(raw_response_text, strict=False)
+            return TaskRelevance(**parsed_json)
+        except (json.JSONDecodeError, ValidationError) as e:
+            logger.warning(f"Failed to parse relevance assessment response. Error: {e}")
+            return None
+            
+    except Exception as e:
+        logger.error(f"Critical error during relevance assessment LLM call: {e}", exc_info=True)
+        return None