Update app/main.py

app/main.py

@@ -7,31 +7,23 @@ import asyncio
 from typing import List, Optional, Dict
 from concurrent.futures import ThreadPoolExecutor
 from dotenv import load_dotenv
-
 from fastapi import FastAPI, HTTPException, status
 from pydantic import BaseModel
 import uvicorn
 
-# Load environment variables
 load_dotenv()
 
-# Import internal services
 from app.predictor import classifier, guide_generator, reviewer
 from app.core.model_loader import llm_engine
 
-# 1. Setup Logging
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
-# 2. Initialize FastAPI
 app = FastAPI(title="GitGud AI Service")
 
-# Global embedding cache
 REPO_CACHE: Dict[str, Dict[str, List[float]]] = {}
+executor = ThreadPoolExecutor(max_workers=8)
 
-executor = ThreadPoolExecutor(max_workers=4)
-
-# 3. Data Models
 class FileRequest(BaseModel):
     fileName: str
     content: Optional[str] = None
@@ -54,7 +46,6 @@ class ChatRequest(BaseModel):
     context: List[Dict[str, str]]
     repoName: str
 
-# 4. Core Logic Helpers
 def calculate_repo_health(total_vulns: int, avg_maint: float) -> int:
     base_score = avg_maint * 10
     penalty = total_vulns * 8
@@ -62,25 +53,17 @@ def calculate_repo_health(total_vulns: int, avg_maint: float) -> int:
 
 def sync_review_worker(file_list: List[FileRequest]):
     logger.info(f"--- [DEBUG] Processing {len(file_list)} files for code review ---")
-    for f in file_list:
-        content_len = len(f.content or "")
-        logger.info(f"File: {f.fileName} | Content length: {content_len} chars")
-
     try:
         results = reviewer.service.review_batch_code(file_list)
-        logger.info(f"--- [DEBUG] Reviewer Output: {results} ---")
         return results
     except Exception as e:
         logger.error(f"--- [ERROR] Exception during review processing: {e} ---", exc_info=True)
         raise e
 
 def parse_tree_to_list(raw_tree: str):
-    """Parses text tree into JSON objects for Compose LazyColumn"""
     nodes = []
     for line in raw_tree.strip().split('\n'):
-        # Detect depth based on tree characters
         level = line.count('|') + (line.count(' ') // 2)
-        # Clean the name
         name = re.sub(r'[|└├─]', '', line).strip()
         if name:
             nodes.append({
@@ -90,8 +73,6 @@ def parse_tree_to_list(raw_tree: str):
             })
     return nodes
 
-# 5. Endpoints
-
 @app.get("/")
 def health_check():
     return {
@@ -113,7 +94,6 @@ async def classify_file(request: FileRequest):
             if request.repoName not in REPO_CACHE:
                 REPO_CACHE[request.repoName] = {}
             REPO_CACHE[request.repoName][request.fileName] = result["embedding"]
-
         return {
             "fileName": request.fileName,
             "layer": result["label"],
@@ -151,7 +131,6 @@ async def get_dashboard_stats(request: BatchReviewRequest):
         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)
 
@@ -184,7 +163,6 @@ async def analyze_file(request: FileRequest):
         result = classifier.predict(request.fileName, request.content)
         summary = classifier.generate_file_summary(request.content, request.fileName)
         tags = classifier.extract_tags(request.content, request.fileName)
-
         return {
             "fileName": request.fileName,
             "layer": result["label"],
@@ -214,35 +192,27 @@ async def semantic_search(request: SearchRequest):
 
 @app.post("/chat")
 async def chat(request: ChatRequest):
-    start_time = time.time()
     try:
         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}
-        """
+
+        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:
         if "429" in str(e):
-            # Return a structured error so Compose can show a nice UI
             return {"response": "⚠️ Daily limit reached. Try again in a bit!", "status": "quota_error"}
         raise HTTPException(status_code=500, detail=str(e))
 
 @app.post("/generate-guide")
 async def generate_guide(request: GuideRequest):
-    """
-    FIXED: Now parses the messy tree text into JSON for your Compose UI.
-    """
     try:
         markdown = guide_generator.generate_markdown(request.repoName, request.filePaths)
-        
-        # Extract the messy tree part and clean it
         tree_match = re.search(r"Project Structure\n\n(.*?)(?=\n\n|$)", markdown, re.S)
         structured_tree = []
         if tree_match:
@@ -258,7 +228,6 @@ async def generate_guide(request: GuideRequest):
             raise HTTPException(status_code=429, detail="AI Quota Exceeded")
         raise HTTPException(status_code=500, detail=str(e))
 
-# 6. Application Entry Point
 if __name__ == "__main__":
     port = int(os.environ.get("PORT", 7860))
     uvicorn.run(app, host="0.0.0.0", port=port)