--- a
+++ b/utils/metrics.py
@@ -0,0 +1,76 @@
+import pandas as pd
+import pandas as pd
+
+from .pycocoevalcap.bleu.bleu import Bleu
+from .pycocoevalcap.meteor import Meteor
+from .pycocoevalcap.rouge import Rouge
+import re
+
+import errno
+import os
+
+try:
+    os.mkdir('results')
+except OSError as exc:
+    if exc.errno != errno.EEXIST:
+        raise
+    pass
+
+def preprocess_captions(images_captions):
+    """
+    :param images_captions: Dictionary with image ids as keys and captions as values
+    :return: Dictionary with the processed captions as values
+    """
+
+    # Clean for BioASQ
+    bioclean = lambda t: re.sub('[.,?;*!%^&_+():-\[\]{}]', '',
+                                t.replace('"', '').replace('/', '').replace('\\', '').replace("'",
+                                                                                              '').strip().lower())
+    pr_captions = {}
+    # Apply bio clean to data
+    for image in images_captions:
+        # Save caption to an array to match MSCOCO format
+        pr_captions[image] = [bioclean(images_captions[image])]
+
+    return pr_captions
+
+def compute_scores(gts:str, res:str, save_scores:bool=True):
+    """
+    Performs the MS COCO evaluation using the Python 3 implementation (https://github.com/salaniz/pycocoevalcap)
+
+    :param gts: Dictionary with the image ids and their gold captions,
+    :param res: Dictionary with the image ids ant their generated captions
+    :print: Evaluation score (the mean of the scores of all the instances) for each measure
+    """
+    # convert pd.Dataframe to dict
+    gold_captions_df = pd.read_csv(gts, sep='|', names=['ID', 'caption'])
+    pred_captions_df = pd.read_csv(res, sep='|', names=['ID', 'caption'])
+    
+    gold_captions = preprocess_captions(dict( zip( gold_captions_df.ID.to_list(), gold_captions_df.caption.to_list() ) ) )
+    pred_captions = preprocess_captions( dict( zip( pred_captions_df.ID.to_list(), pred_captions_df.caption.to_list() ) ) )
+    
+
+    # Set up scorers
+    scorers = [
+        (Bleu(4), ["BLEU_1", "BLEU_2", "BLEU_3", "BLEU_4"]),
+        (Meteor(), "METEOR"),
+        (Rouge(), "ROUGE_L")
+    ]
+    metrics_scores = {}
+    # Compute score for each metric
+    for scorer, method in scorers:
+        try:
+            score, scores = scorer.compute_score(gold_captions, pred_captions, verbose=0)
+        except TypeError:
+            score, scores = scorer.compute_score(gold_captions, pred_captions)
+        if type(method) == list:
+            for sc, m in zip(score, method):
+                metrics_scores[m] = [round(sc*100, 1)]
+        else:
+            metrics_scores[method] = [round(score*100, 1)]
+
+    if save_scores:
+        scores_df = pd.DataFrame.from_dict(metrics_scores)
+        scores_df.to_csv('results/scores.csv', sep='\t')
+
+    return metrics_scores
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