Add requirements

app.py

@@ -0,0 +1,116 @@
+import gradio as gr
+import nltk
+import re
+nltk.download('punkt')
+nltk.download('wordnet')
+from nltk.corpus import wordnet
+nltk.download("averaged_perceptron_tagger")
+nltk.download('wordnet')
+
+
+def find_antonyms(word, tag):
+    if tag.startswith('JJ') or (tag.startswith('RB') and word not in ["not", "no"]) or (tag.startswith('VB') and word not in ["be", "have", "do", "will", "shall",  "are", "is", "was", "were"]):
+        antonyms = []
+        for syn in wordnet.synsets(word):
+            for lemma in syn.lemmas():
+                if lemma.antonyms():
+                    antonyms.append(lemma.antonyms()[0].name())
+
+        return antonyms
+
+    return []
+
+
+def insert_not(sentence):
+    words = nltk.word_tokenize(sentence)
+    pos_tags = nltk.pos_tag(words)
+
+    aux_verbs = ["be", "have", "do", "will", "shall", "are", "is", "was", "were", "am", "does", "has", "had", "can", "could", "may", "might", "must", "should", "would"]
+
+    new_words = []
+    #skip_next = False
+
+    flag = False
+
+    for i, (word, tag) in enumerate (pos_tags):
+        new_words.append(word)
+        if word.lower() in aux_verbs:
+            if i + 1 < len(words) and words[i+1].lower() != 'not' and not flag:
+                new_words.append("not")
+                flag = True
+
+    return " ".join(new_words)
+
+
+def modify_sentence(sentence):
+    words = nltk.word_tokenize(sentence)
+    pos_tagger = nltk.pos_tag(words)
+
+    new_sentence = insert_not(sentence)
+
+    if len(new_sentence) != len(sentence):
+        return new_sentence
+    else:
+        new_words = []
+        for word, tag in pos_tagger:
+            antonyms = find_antonyms(word, tag)
+            if antonyms:
+                new_word = antonyms[0]
+            else:
+                new_word = word
+
+            new_words.append(new_word)
+
+        new_sentence = " ".join(new_words)
+        # print(sentence)
+        # print(new_sentence)
+
+        if new_sentence == sentence:
+            new_sentence = insert_not(sentence)
+
+        return new_sentence
+
+
+
+def remove_no_not(sentence):
+    words_to_remove = ['no', 'not']
+
+    pattern = r'\b(' + '|'.join(words_to_remove) + r')\b'
+    cleaned_sentence = re.sub(pattern, '', sentence)
+
+    cleaned_sentence = re.sub(r'\s+', ' ', cleaned_sentence).strip()
+    # print(cleaned_sentence)
+    return cleaned_sentence
+
+
+
+def clean_text(text):
+    text = re.sub(r'[^\w\s]', '', text)
+    text = text.strip()
+
+    return text
+
+def main_func(sentence):
+    sentence = clean_text(sentence)
+
+    removed_no_sentence = remove_no_not(sentence)
+    # print(removed_no_sentence)
+
+    if len(removed_no_sentence) != len(sentence):
+        print("Original:", sentence)
+        print("Modified:", removed_no_sentence)
+        return removed_no_sentence
+    else:
+        modified_sentence = modify_sentence(sentence)
+        print("Original:", sentence)
+        print("Modified:", modified_sentence)
+
+        return modified_sentence
+
+    
+
+
+demo = gr.Interface(fn=main_func, inputs="text", outputs="text")
+    
+if __name__ == "__main__":
+    demo.launch(show_api=False)