started setting it up, needs some ironing out but almost good to go

src/malco/analysis/ita_grounding_analysis.py

@@ -0,0 +1,79 @@
+from malco.post_process.post_process_results_format import read_raw_result_yaml
+from pathlib import Path
+import pandas as pd
+import os
+# Each row has
+#    c1      *       c2         *  c3  *       c4         *        c5              *            c6              *  c7                       * c8
+# PMID (str) * label/term (str) * rank * ita_reply (bool) * correct_result OMIM ID * correct_result OMIM label  *  MONDO ID (if applicable) * correct? 0/1 (in excel)
+
+# Correct results
+file = "/Users/leonardo/git/malco/in_ita_reply/correct_results.tsv"
+answers = pd.read_csv(
+        file, sep="\t", header=None, names=["description", "term", "label"]
+    )
+
+# Mapping each label to its correct term
+cres = answers.set_index("label").to_dict() # Cleanup this fella TODO
+
+# Just populate df with two for loops, then sort alfabetically
+data = []
+
+# load ita replies
+ita_file = Path("/Users/leonardo/git/malco/out_itanoeng/raw_results/multilingual/it/results.yaml")
+ita_result = read_raw_result_yaml(ita_file)
+
+# extract input_text from yaml for ita, or extracted_object, terms
+for ppkt_out in ita_result:
+    extracted_object = ppkt_out.get("extracted_object")
+    if extracted_object:
+        label = extracted_object.get("label").replace('_it-prompt', '_en-prompt')
+        terms = extracted_object.get("terms")
+        if terms:
+            num_terms = len(terms)
+            rank_list = [i + 1 for i in range(num_terms)]
+            for term, rank in zip(terms, rank_list):
+                data.append({"pubmedid": label, "term": term, "rank": rank, "ita_reply": True, "correct_omim_id": cres[label][0], 
+                             "correct_omim_id": cres[label][1], "mondo_id": float('Nan')})
+
+
+# load eng replies
+eng_file = Path("/Users/leonardo/git/malco/out_itanoeng/raw_results/multilingual/it_w_en/results.yaml")
+eng_result = read_raw_result_yaml(eng_file)
+
+# extract named_entities, id and label from yaml for eng
+# extract input_text from yaml for ita, or extracted_object, terms
+for ppkt_out in eng_result:
+    extracted_object = ppkt_out.get("extracted_object")
+    if extracted_object:
+        label = extracted_object.get("label")#.str.replace('_[a-z][a-z]-prompt', '', regex=True)
+        terms = extracted_object.get("terms")
+        if terms:
+            num_terms = len(terms)
+            rank_list = [i + 1 for i in range(num_terms)]
+            for term, rank in zip(terms, rank_list):
+                if term.str.startswith("MONDO"):
+                    breakpoint()
+                    ne = ppkt_out.get("named_entities")
+                    mid = ne.get("id")
+                    mlab = ne.get("label") # TODO finish
+                else:
+                    mlab = float('Nan')
+
+                data.append({"pubmedid": label, "term": term, "rank": rank, "ita_reply": False, "correct_omim_id": cres[label][0], 
+                             "correct_omim_id": cres[label][1], "mondo_id": mlab})
+
+# Create DataFrame
+column_names = [
+    "PMID",
+    "diagnosis",
+    "rank",
+    "ita_reply",
+    "correct_OMIMid",
+    "correct_OMIMlabel",
+    "MONDOid",
+]
+
+df = pd.DataFrame(data, columns=column_names)
+df = df.sort_values(by = 'Name') 
+
+#df.to_excel(os.getcwd() + "ita_replies2curate.xlsx")