Update app/main.py

app/main.py

@@ -1,44 +1,47 @@
-# entry point aka building FAST_API here
-
+# main.py - Final Fixed Version
 from fastapi import FastAPI, HTTPException
 from pydantic import BaseModel
+from typing import List, Optional  
+import logging
 
+from app.services.reviewer_service import AIReviewerService
 from app.predictor import classifier, guide_generator
 
-app = FastAPI(title="GitGud AI Service")
+# 1. Setup Logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
 
+# 2. Initialize FastAPI and Services
+app = FastAPI(title="GitGud AI Service")
+reviewer_service = AIReviewerService()
 
-# Data Model: Matches what NestJS (server-side[refer to visualization.services.ts]) sends
+# 3. Data Models (Order matters: ReviewRequest needs FileRequest)
 class FileRequest(BaseModel):
     fileName: str
-    content: str | None = None
+    content: Optional[str] = None
 
+class ReviewRequest(BaseModel):
+    files: List[FileRequest]
 
 class GuideRequest(BaseModel):
     repoName: str
-    filePaths: list[str]
+    filePaths: List[str]
 
+# 4. Endpoints
 
 @app.get("/")
 def health_check():
-    """
-    Simple check to see if the server is alive and which GPU it's using.
-    """
+    """Checks server status and GPU availability."""
     return {
         "status": "online",
         "model": "microsoft/codebert-base",
         "device": classifier.device,
     }
 
-
-# first FAST_API with endpoint('/classify') called in [visualization.services.ts]
-# @param {*} file
-# @return {*} layerd based classified_info along with file-name
 @app.post("/classify")
 async def classify_file(request: FileRequest):
+    """Classifies file into architectural layers."""
     try:
-        # calling the predict function of our classifier to determine which layer it belongs
-        # returns { label, confidence, embedding }
         result = classifier.predict(request.fileName, request.content)
         return {
             "fileName": request.fileName, 
@@ -47,20 +50,32 @@ async def classify_file(request: FileRequest):
             "embedding": result["embedding"]
         }
     except Exception as e:
+        logger.error(f"Classify failed: {e}")
         raise HTTPException(status_code=500, detail=str(e))
 
-
 @app.post("/generate-guide")
 async def generate_guide(request: GuideRequest):
+    """Generates markdown guides for repositories."""
     try:
         markdown = guide_generator.generate_markdown(request.repoName, request.filePaths)
         return {"markdown": markdown}
     except Exception as e:
+        logger.error(f"Guide generation failed: {e}")
         raise HTTPException(status_code=500, detail=str(e))
 
+@app.post("/review")
+async def review_code(request: ReviewRequest):
+    """Detects security and logic issues in batches of files."""
+    try:
+        # Call the batch review logic from your service
+        results = reviewer_service.review_batch_code(request.files)
+        return {"reviews": results}
+    except Exception as e:
+        logger.error(f"Review endpoint failed: {e}")
+        raise HTTPException(status_code=500, detail=str(e))
 
+# 5. Application Entry Point
 if __name__ == "__main__":
     import uvicorn
-
-    # Runs on localhost:8000
-    uvicorn.run(app, host="0.0.0.0", port=8000)
+    # Port 7860 is mandatory for Hugging Face Spaces
+    uvicorn.run(app, host="0.0.0.0", port=7860)