Upload emotions_dataset.py

emotions_dataset.py

@@ -1,3 +1,4 @@
+import os
 import zipfile
 from typing import List
 
@@ -133,17 +134,24 @@ class EmotionsDataset(datasets.GeneratorBasedBuilder):
             emo_mapping = {0: "no emotion", 1: "anger", 2: "disgust",
                            3: "fear", 4: "happiness", 5: "sadness", 6: "surprise"}
             for i, filepath in enumerate(filepaths):
-                with zipfile.ZipFile(filepath, 'r') as archive:
-                    emotions = archive.open("ijcnlp_dailydialog/dialogues_emotion.txt", "r").read().decode().split("\n")
-                    text = archive.open("ijcnlp_dailydialog/dialogues_text.txt", "r").read().decode().split("\n")
-                    for idx_out, (e, t) in enumerate(zip(emotions, text)):
-                        if len(t.strip()) > 0:
-                            cast_emotions = [int(j) for j in e.strip().split(" ")]
-                            cast_dialog = [d.strip() for d in t.split("__eou__") if len(d)]
-                            for idx_in, (ce, ct) in enumerate(zip(cast_emotions, cast_dialog)):
-                                uid = f"daily_dialog_{i}_{idx_out}_{idx_in}"
-                                yield uid, {"text": ct,
-                                            "id": uid,
-                                            "dataset": dataset,
-                                            "license": license,
-                                            "label": emo_mapping[ce]}
+                if os.path.isdir(filepath):
+                    emotions = open(os.path.join(filepath, "ijcnlp_dailydialog/dialogues_emotion.txt"), "r").read()
+                    text = open(os.path.join(filepath, "ijcnlp_dailydialog/dialogues_text.txt"), "r").read()
+                else:
+                    archive = zipfile.ZipFile(filepath, 'r')
+                    emotions = archive.open("ijcnlp_dailydialog/dialogues_emotion.txt", "r").read().decode()
+                    text = archive.open("ijcnlp_dailydialog/dialogues_text.txt", "r").read().decode()
+                emotions = emotions.split("\n")
+                text = text.split("\n")
+
+                for idx_out, (e, t) in enumerate(zip(emotions, text)):
+                    if len(t.strip()) > 0:
+                        cast_emotions = [int(j) for j in e.strip().split(" ")]
+                        cast_dialog = [d.strip() for d in t.split("__eou__") if len(d)]
+                        for idx_in, (ce, ct) in enumerate(zip(cast_emotions, cast_dialog)):
+                            uid = f"daily_dialog_{i}_{idx_out}_{idx_in}"
+                            yield uid, {"text": ct,
+                                        "id": uid,
+                                        "dataset": dataset,
+                                        "license": license,
+                                        "label": emo_mapping[ce]}