Update app.py

app.py

@@ -14,6 +14,38 @@ import pandas as pd
 from sklearn.metrics import f1_score
 import seaborn as sns
 
+import torch
+import numpy as np
+import random
+import os
+
+# Set a fixed random seed for reproducibility
+seed = 42
+random.seed(seed)
+np.random.seed(seed)
+torch.manual_seed(seed)  # Ensure PyTorch random seed for CPU
+
+# If running on GPU, set the seed for CUDA as well
+if torch.cuda.is_available():
+    torch.cuda.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+    torch.backends.cudnn.deterministic = True
+    torch.backends.cudnn.benchmark = False
+
+# Ensure that the model uses float32 precision (same as on GPU)
+# Assuming you load your model later, ensure it's float precision.
+# model = model.float()  # Uncomment this when loading the model
+
+# Enable deterministic algorithms in PyTorch (slower, but ensures consistency)
+torch.use_deterministic_algorithms(True)
+
+# Limit the number of threads to prevent non-deterministic results from multithreading
+torch.set_num_threads(1)
+os.environ['MKL_NUM_THREADS'] = '1'
+os.environ['OMP_NUM_THREADS'] = '1'
+
+# Optional: Use for debugging to ensure intermediate values match across devices
+torch.set_printoptions(precision=10)
 
 #################### BEAM PREDICTION #########################}
 def beam_prediction_task(data_percentage, task_complexity):