Update app/main.py

app/main.py

@@ -62,8 +62,13 @@ def sync_review_worker(file_list: List[FileRequest]):
 def parse_tree_to_list(raw_tree: str):
     nodes = []
     for line in raw_tree.strip().split('\n'):
+        if line.startswith("```") or not line.strip():
+            continue
         level = line.count('|') + (line.count(' ') // 2)
+        # Strip tree connectors
         name = re.sub(r'[|└├─]', '', line).strip()
+        # Strip AI layer annotations like [Backend] or [Frontend]
+        name = re.sub(r'\[.*?\]', '', name).strip()
         if name:
             nodes.append({
                 "name": name,
@@ -121,28 +126,23 @@ async def get_dashboard_stats(request: BatchReviewRequest):
         raw_reviews = await loop.run_in_executor(executor, sync_review_worker, request.files)
         if not isinstance(raw_reviews, list):
             raw_reviews = [raw_reviews]
-
         total_vulns = 0
         maint_scores = []
         found_apis = set()
         api_regex = re.compile(r'(?:get|post|put|delete|patch)\([\'"]\/(.*?)[\'"]', re.IGNORECASE)
-
         for i, current_review in enumerate(raw_reviews):
             vulns = current_review.get("vulnerabilities", [])
             total_vulns += len(vulns)
             m_score = current_review.get("metrics", {}).get("maintainability", 8.0)
             maint_scores.append(m_score)
-
             content = request.files[i].content if i < len(request.files) else None
             if content:
                 matches = api_regex.findall(content)
                 for match in matches:
                     found_apis.add(f"/{match}")
-
         num_files = len(maint_scores)
         avg_maint = (sum(maint_scores) / num_files) if num_files > 0 else 0.0
         health_score = calculate_repo_health(total_vulns, avg_maint)
-
         return {
             "repo_health": health_score,
             "health_label": "Excellent" if health_score > 85 else "Good" if health_score > 60 else "Critical",
@@ -180,10 +180,8 @@ async def semantic_search(request: SearchRequest):
         embeddings = request.embeddings
         if not embeddings and request.repoName and request.repoName in REPO_CACHE:
             embeddings = REPO_CACHE[request.repoName]
-
         if not embeddings:
             return {"results": []}
-
         results = classifier.semantic_search(request.query, embeddings)
         return {"results": results}
     except Exception as e:
@@ -195,12 +193,10 @@ async def chat(request: ChatRequest):
         context_str = ""
         for item in request.context:
             context_str += f"--- FILE: {item['fileName']} ---\n{item['content']}\n\n"
-
         prompt = f"""You are "GitGud AI", an expert software architect.
 Repository: "{request.repoName}"
 CONTEXT: {context_str if request.context else "(NO CODE PROVIDED)"}
 USER QUESTION: {request.query}"""
-
         response = llm_engine.generate_text(prompt)
         return {"response": response, "status": "success"}
     except Exception as e:
@@ -213,11 +209,9 @@ async def generate_guide(request: GuideRequest):
     try:
         markdown = guide_generator.generate_markdown(request.repoName, request.filePaths)
         tree_match = re.search(r"Project Structure\n\n(.*?)(?=\n\n|$)", markdown, re.S)
-
         structured_tree = []
         if tree_match:
             structured_tree = parse_tree_to_list(tree_match.group(1))
-
         return {
             "markdown": markdown,
             "structured_tree": structured_tree,