Update app/main.py

app/main.py

@@ -8,11 +8,6 @@ from . import configs as config
 from .models import UploadResponse, ClassifyRequest, ClassifyResponse, SegmentRequest, SegmentResponse
 from .services.predictor import classify_image
 from .services.segmenter import segment_image
-from .services.routing import (
-    router as model_router,
-    resolve_classifier_version,
-    get_ab_status,
-)
 from .storage import (
     save_image,
     get_image_path,
@@ -26,14 +21,11 @@ from .monitoring.metrics import MetricsMiddleware, metrics_endpoint
 
 app = FastAPI(
     title="AI Image Classification API",
-    version="3.1.0"
+    version="3.2.0"
 )
 
 app.add_middleware(MetricsMiddleware)
 
-# ✅ Include the router for model management endpoints
-app.include_router(model_router)
-
 ALLOWED_CONTENT_TYPES = {"image/jpeg", "image/png", "image/jpg"}
 MAX_FILE_SIZE = 10 * 1024 * 1024
 
@@ -51,8 +43,6 @@ async def models_status():
     return {
         "classification_loaded": classification_model is not None,
         "segmentation_loaded": segmentation_model is not None,
-        "ab_testing": get_ab_status(),  # ✅ استخدام الدالة المستقلة
-        "loaded_versions": config.get_loaded_versions(),
         "storage_paths": {
             "images": str(config.IMAGES_DIR),
             "segments": str(config.SEGMENTS_DIR),
@@ -60,11 +50,6 @@ async def models_status():
     }
 
 
-@app.get("/api/ab-status")
-async def ab_testing_status():
-    return get_ab_status()  # ✅ استخدام الدالة المستقلة
-
-
 @app.post("/api/upload", status_code=201, response_model=UploadResponse)
 async def upload_image(file: UploadFile = File(...)):
     if file.content_type not in ALLOWED_CONTENT_TYPES:
@@ -112,24 +97,20 @@ async def classify(body: ClassifyRequest):
     if image_bytes is None:
         raise HTTPException(status_code=404, detail="Image not found")
 
+    # ✅ Check for cached result
     existing = get_classification_result(body.image_id)
-    if existing and not body.model_version:
+    if existing:
         return ClassifyResponse(
             image_id=body.image_id,
             prediction=existing["prediction"],
             confidence=existing["confidence"],
-            model_version=existing["model_version"],
+            model_version=existing.get("model_version", "hf_savedmodel"),
             status=existing.get("status", "completed"),
         )
 
-    # ✅ استخدام الدالة المستقلة
-    resolved_uri = resolve_classifier_version(body.model_version)
-
+    # ✅ Classify without model_version
     try:
-        prediction, confidence, actual_version = classify_image(
-            image_bytes,
-            model_version=resolved_uri,
-        )
+        prediction, confidence = classify_image(image_bytes)
     except ValueError as e:
         raise HTTPException(status_code=400, detail=str(e))
     except Exception as e:
@@ -138,7 +119,7 @@ async def classify(body: ClassifyRequest):
     result = {
         "prediction": prediction,
         "confidence": confidence,
-        "model_version": actual_version,
+        "model_version": "hf_savedmodel",
         "status": "completed",
     }
     save_classification_result(body.image_id, result)
@@ -159,7 +140,7 @@ async def get_classify_result(image_id: str):
         image_id=image_id,
         prediction=result["prediction"],
         confidence=result["confidence"],
-        model_version=result["model_version"],
+        model_version=result.get("model_version", "hf_savedmodel"),
         status=result.get("status", "completed"),
     )
 
@@ -170,6 +151,7 @@ async def segment(body: SegmentRequest):
     if image_bytes is None:
         raise HTTPException(status_code=404, detail="Image not found")
 
+    # ✅ Check for cached result
     existing = get_segmentation_result(body.image_id)
     if existing:
         return SegmentResponse(
@@ -181,6 +163,7 @@ async def segment(body: SegmentRequest):
             error=existing.get("error"),
         )
 
+    # ✅ Run segmentation
     seg_result = segment_image(image_bytes)
     seg_path = save_segmentation_result(body.image_id, seg_result)
 
@@ -217,7 +200,7 @@ async def root():
 
     return {
         "service": "AI Image Classification API",
-        "version": "3.1.0",
+        "version": "3.2.0",
         "classification": class_ready,
         "segmentation": seg_ready,
         "endpoints": {
@@ -226,7 +209,6 @@ async def root():
             "segment": "POST /api/segment",
             "health": "GET /health",
             "metrics": "GET /metrics",
-            "ab_status": "GET /api/ab-status",
             "docs": "/docs",
         },
         "models_status": "/models/status",