toxicity-detector

nlp/replying.py

@@ -0,0 +1,59 @@
+#  "👍", "👎", "❤", "🔥", "🥰", "👏", "😁",
+# "🤔", "🤯", "😱", "🤬", "😢", "🎉", "🤩",
+# "🤮", "💩", "🙏", "👌", "🕊", "🤡", "🥱",
+# "🥴", "😍", "🐳", "❤‍🔥", "🌚", "🌭", "💯",
+# "🤣", "⚡", "🍌", "🏆", "💔", "🤨", "😐",
+# "🍓", "🍾", "💋", "🖕", "😈", "😴", "😭",
+# "🤓", "👻", "👨‍💻", "👀", "🎃", "🙈", "😇",
+# "😨", "🤝", "✍", "🤗", "🫡", "🎅", "🎄",
+# "☃", "💅", "🤪", "🗿", "🆒", "💘", "🙉",
+# "🦄", "😘", "💊", "🙊", "😎", "👾", "🤷‍♂",
+# "🤷", "🤷‍♀", "😡"
+
+toxic_reactions = {
+    "071": "🕊",
+    "073": "👀",
+    "075": "🙈",
+    "077": "🙊",
+    "079": "💩",
+    "081": "🤔",
+    "083": "😐",
+    "085": "🤨",
+    "087": "🥴",
+    "089": "🤯",
+    "091": "😢",
+    "093": "😭",
+    "095": "😨",
+    "097": "😱",
+    "099": "🤬"
+}
+
+grads = list(toxic_reactions.keys())
+grads.sort()
+grads.reverse()
+
+abusive_reactions = {
+    "085": "🫡",
+    "088": "💅",
+    "091": "🤷‍♀",
+    "094": "👾",
+    "097": "👻",
+    "099": "😈"
+}
+
+abusive_grads = list(abusive_reactions.keys())
+abusive_grads.sort()
+abusive_grads.reverse()
+
+def get_toxic_reply(tx):
+    percentage = tx * 100
+    for key in grads:
+        if percentage > int(key):
+            return toxic_reactions[key]
+
+
+def get_abusive_reply(tx):
+    percentage = tx * 100
+    for key in abusive_grads:
+        if percentage > int(key):
+            return abusive_reactions[key]