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utils/cache_manager.py

@@ -0,0 +1,49 @@
+from functools import lru_cache
+from typing import Iterable, List, Tuple
+
+
+BATCH_SIZE = 50
+
+
+def _ensure_tuple(labels: Iterable[str]) -> Tuple[str, ...]:
+    if isinstance(labels, tuple):
+        return labels
+    return tuple(labels)
+
+
+@lru_cache(maxsize=5)
+def cached_inference(image_path, labels, model, processor):
+    import torch
+    from PIL import Image
+
+    label_tuple: Tuple[str, ...] = _ensure_tuple(labels)
+
+    with Image.open(image_path).convert("RGB") as img:
+        tensor_image = img.copy()
+
+    device = next(model.parameters()).device
+    dtype = next(model.parameters()).dtype
+    logits: List[float] = []
+
+    with torch.no_grad():
+        for start in range(0, len(label_tuple), BATCH_SIZE):
+            batch = label_tuple[start : start + BATCH_SIZE]
+            inputs = processor(images=tensor_image, text=list(batch), return_tensors="pt", padding=True)
+
+            prepared = {}
+            for key, value in inputs.items():
+                if torch.is_tensor(value):
+                    moved = value.to(device)
+                    if torch.is_floating_point(moved):
+                        moved = moved.to(dtype=dtype)
+                    prepared[key] = moved
+                else:
+                    prepared[key] = value
+
+            outputs = model(**prepared)
+            batch_logits = outputs.logits_per_image[0].detach().cpu().tolist()
+            logits.extend(batch_logits)
+
+    tensor_image.close()
+    scores = torch.softmax(torch.tensor(logits), dim=0).tolist()
+    return scores

utils/modality_router.py

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+import numpy as np
+from PIL import Image
+from transformers import pipeline
+
+_router = None
+
+
+def init_router():
+    global _router
+    if _router is None:
+        _router = pipeline("image-classification", model="Matthijs/mobilevit-small")
+
+
+def detect_modality(image_path: str) -> str:
+    """Hybrid modality detection: filename hints + visual + lightweight model"""
+    name = image_path.lower()
+    # 1️⃣ تحليل الاسم
+    if any(k in name for k in ["xray", "chest", "lung"]):
+        return "xray"
+    if any(k in name for k in ["ct", "abdomen", "liver"]):
+        return "ct"
+    if any(k in name for k in ["ultrasound", "us", "sonogram"]):
+        return "ultrasound"
+    if any(k in name for k in ["mri", "brain", "spine"]):
+        return "mri"
+    if any(k in name for k in ["histopath", "slide", "micro"]):
+        return "pathology"
+    if any(k in name for k in ["skin", "derma"]):
+        return "skin"
+    if any(k in name for k in ["eye", "retina", "fundus"]):
+        return "eye"
+    if any(k in name for k in ["cardio", "echo", "heart"]):
+        return "cardio"
+    if any(k in name for k in ["msk", "musculoskeletal", "orthopedic"]):
+        return "musculoskeletal"
+
+    # 2️⃣ تحليل بصري بسيط
+    with Image.open(image_path).convert("RGB") as img:
+        arr = np.array(img)
+    mean_sat = np.std(arr, axis=(0, 1)).mean()
+    if mean_sat < 15:
+        return "xray"
+    elif mean_sat < 25:
+        return "mri"
+    elif mean_sat > 45:
+        return "skin"
+
+    # 3️⃣ نموذج صغير fallback
+    if _router is None:
+        init_router()
+    pred = _router(image_path)[0]["label"].lower()
+    valid = {
+        "xray",
+        "ct",
+        "ultrasound",
+        "mri",
+        "pathology",
+        "skin",
+        "eye",
+        "cardio",
+        "musculoskeletal",
+    }
+    return "general" if pred not in valid else pred