feat: Add model caching to batch_predict endpoint to eliminate redund…

app/services/gaze_tracker.py

@@ -1,4 +1,6 @@
 # Necessary imports
+import os
+import threading
 import math
 import warnings
 
@@ -42,7 +44,7 @@
     func_total_accuracy,
 )
 from app.services.config import hyperparameters
-
+from collections import defaultdict,OrderedDict
 
 # Machine learning models to use
 models = {
@@ -76,6 +78,32 @@
     "mae": make_scorer(mean_absolute_error),
 }
 
+class LRUCache:
+    def __init__(self, max_size=100): 
+        self.max_size = max_size
+        self.cache = OrderedDict()
+
+    def get(self, key):
+        if key in self.cache:
+            # Move to end
+            self.cache.move_to_end(key)
+            return self.cache[key]
+        return None
+
+    def put(self, key, value):
+        if key in self.cache:
+            self.cache.move_to_end(key)
+        elif len(self.cache) >= self.max_size:
+            # Remove LRU
+            self.cache.popitem(last=False)
+
+        self.cache[key] = value
+
+    def __contains__(self, key):
+        return key in self.cache
+
+model_cache=LRUCache(max_size=100)
+calibration_locks = defaultdict(threading.Lock)
 
 def squash(v, limit=1.0):
     """Squash não-linear estilo WebGazer"""
@@ -249,6 +277,8 @@ def predict_new_data_simple(
     SQUASH_LIMIT_X = 1.0
     SQUASH_LIMIT_Y = 1.0
     Y_GAIN = 1.2  # adjustment to compensate for vertical bias
+    csv_filename = os.path.basename(calib_csv_path)  
+    calib_id = csv_filename.replace("_fixed_train_data.csv", "")
 
     # ============================
     # LOAD TRAIN
@@ -309,14 +339,31 @@ def predict_new_data_simple(
         diff_y_norm, rel_y_norm
     ])
 
-    # ============================
-    # MODELS
-    # ============================
-    model_x = make_pipeline(StandardScaler(), Ridge(alpha=1.0))
-    model_y = make_pipeline(StandardScaler(), Ridge(alpha=1.0))
+    with calibration_locks[calib_id]:
+        cached_models = model_cache.get(calib_id)
+
+        if cached_models:
+            print(f'Loading models from cache')
+            model_x = cached_models.get('x')
+            model_y = cached_models.get('y')
+        else:  
+
+            # ============================
+            # MODELS
+            # ============================
+            model_x = make_pipeline(StandardScaler(), Ridge(alpha=1.0))
+            model_y = make_pipeline(StandardScaler(), Ridge(alpha=1.0))
+
+            model_x.fit(X_train_x, y_train_x)
+            model_y.fit(X_train_y, y_train_y)   
+
+            model_cache.put(calib_id,{
+                "x":model_x,
+                "y":model_y
+            })
+
+
 
-    model_x.fit(X_train_x, y_train_x)
-    model_y.fit(X_train_y, y_train_y)
 
     # ============================
     # Real scale (calibration) - normalize predicted values to screen coordinates