--- a
+++ b/src/compute_metric_results.py
@@ -0,0 +1,97 @@
+import csv
+import sys
+import os
+import numpy as np
+from src.config import BratsConfiguration
+from src.dataset.utils import dataset, nifi_volume as nifi_utils
+from src.dataset import brats_labels
+from src.metrics import evaluation_metrics as eval
+from tqdm import tqdm
+
+
+def compute(volume_pred, volume_gt, roi_mask):
+
+    tp, fp, tn, fn = eval.get_confusion_matrix(volume_pred, volume_gt, roi_mask)
+
+    if len(np.unique(volume_pred)) == 1 and len(np.unique(volume_gt)) == 1 and np.unique(volume_pred)[0] == 0 and np.unique(volume_gt)[0] == 0:
+        print("There is no tumor for this region")
+        dc = 1.0
+        hd = 0.0
+    else:
+        dc = eval.dice(tp, fp, fn)
+        hd = eval.hausdorff(volume_pred, volume_gt)
+
+    recall  = eval.recall(tp, fn)
+    precision = eval.precision(tp, fp)
+    acc = eval.accuracy(tp, fp, tn, fn)
+    f1 = eval.fscore(tp, fp, tn ,fn)
+
+    return dc, hd, recall, precision, acc, f1, (tp, fp, tn, fn)
+
+
+def compute_wt_tc_et(prediction, reference, flair):
+
+    metrics = []
+    roi_mask = dataset.create_roi_mask(flair)
+
+    for typee in ["wt", "tc", "et"]:
+
+        if typee == "wt":
+            volume_gt = brats_labels.get_wt(reference)
+            volume_pred = brats_labels.get_wt(prediction)
+
+        elif typee == "tc":
+            volume_gt = brats_labels.get_tc(reference)
+            volume_pred = brats_labels.get_tc(prediction)
+
+        elif typee == "et":
+            volume_gt = brats_labels.get_et(reference)
+            volume_pred = brats_labels.get_et(prediction)
+            if len(np.unique(volume_gt)) == 1 and np.unique(volume_gt)[0] == 0:
+                print("there is no enchancing tumor region in the ground truth")
+        else:
+            continue
+
+        dc, hd, recall, precision, acc, f1, conf_matrix = compute(volume_pred, volume_gt, roi_mask)
+        metrics.extend([dc, hd, recall, precision, f1])
+
+    return metrics
+
+
+if __name__ == "__main__":
+    config = BratsConfiguration(sys.argv[1])
+    model_config = config.get_model_config()
+    dataset_config = config.get_dataset_config()
+    basic_config = config.get_basic_config()
+
+    data_train, data_test = dataset.read_brats(dataset_config.get("train_csv"))
+    data = data_test
+
+    with open(f"results_test.csv", "w") as file:
+        writer = csv.writer(file)
+        writer.writerow(["subject_ID", "Grade", "Center", "Size",
+                         "Dice WT", "HD WT", "Recall WT", "Precision WT", "F1 WT",
+                         "Dice TC", "HD TC", "Recall TC", "Precision TC", "F1 TC",
+                         "Dice ET", "HD ET", "Recall ET", "Precision ET", "F1 ET"
+                         ])
+
+        for patient in tqdm(data, total=len(data)):
+
+            patient_data = []
+            gt_path = os.path.join(patient.data_path, patient.patient, f"{patient.seg}")
+            data_path = os.path.join(patient.data_path, patient.patient, f"{patient.flair}")
+            prediction_path = os.path.join(patient.data_path, patient.patient, f"{patient.patient}_prediction.nii.gz")
+            if not os.path.exists(prediction_path):
+                print(f"{prediction_path} not found")
+                continue
+
+            patient_data.extend([patient.patient, patient.grade, patient.center, patient.size])
+
+            volume_gt_all, _ = nifi_utils.load_nifi_volume(gt_path)
+            volume_pred_all, _ = nifi_utils.load_nifi_volume(prediction_path)
+            volume, _ = nifi_utils.load_nifi_volume(data_path)
+
+            patient_data = compute_wt_tc_et(volume_pred_all, volume_gt_all, volume)
+            writer.writerow(patient_data)
+
+