Upload app.py

app.py

@@ -81,7 +81,6 @@ def health_check():
 
 @app.post("/predict")
 async def predict_age_gender(file: UploadFile = File(...)):
-    tmp_path = None
     try:
         # Read and decode image
         contents = await file.read()
@@ -91,6 +90,15 @@ async def predict_age_gender(file: UploadFile = File(...)):
         if img is None:
             raise HTTPException(status_code=400, detail="Invalid or unreadable image file.")
 
+        # Apply CLAHE (Contrast Limited Adaptive Histogram Equalization)
+        # This dramatically improves face visibility in bad webcam lighting
+        lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
+        l_channel, a_channel, b_channel = cv2.split(lab)
+        clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
+        cl = clahe.apply(l_channel)
+        limg = cv2.merge((cl, a_channel, b_channel))
+        img = cv2.cvtColor(limg, cv2.COLOR_LAB2BGR)
+
         # Run MiVOLO prediction directly on the numpy image array
         pred = get_predictor()
         detected_objects, _ = pred.recognize(img)
@@ -124,8 +132,3 @@ async def predict_age_gender(file: UploadFile = File(...)):
     except Exception as e:
         logger.error(f"Prediction error: {str(e)}")
         raise HTTPException(status_code=500, detail=str(e))
-
-    finally:
-        # Always clean up temp file
-        if tmp_path and os.path.exists(tmp_path):
-            os.remove(tmp_path)