Update app.py

app.py

@@ -133,14 +133,43 @@ def replace_with_synonym(token):
         return synonym
     return token.text
 
+# Function to check for and avoid double negatives
+def correct_double_negatives(text):
+    doc = nlp(text)
+    corrected_text = []
+    for token in doc:
+        if token.text.lower() == "not" and any(child.text.lower() == "never" for child in token.head.children):
+            # Replace the double negative with a positive statement
+            corrected_text.append("always")
+        else:
+            corrected_text.append(token.text)
+    return ' '.join(corrected_text)
+
+# Function to ensure subject-verb agreement
+def ensure_subject_verb_agreement(text):
+    doc = nlp(text)
+    corrected_text = []
+    for token in doc:
+        if token.dep_ == "nsubj" and token.head.pos_ == "VERB":
+            # Check if the verb agrees with the subject in number
+            if token.tag_ == "NN" and token.head.tag_ != "VBZ":  # Singular noun, should use singular verb
+                corrected_text.append(token.head.lemma_ + "s")
+            elif token.tag_ == "NNS" and token.head.tag_ == "VBZ":  # Plural noun, should not use singular verb
+                corrected_text.append(token.head.lemma_)
+        corrected_text.append(token.text)
+    return ' '.join(corrected_text)
+
 # Function to paraphrase and correct grammar
 def paraphrase_and_correct(text):
-    paraphrased_text = capitalize_sentences_and_nouns(text)  # Capitalize first to ensure proper noun capitalization
+    # Capitalize first to ensure proper noun capitalization
+    paraphrased_text = capitalize_sentences_and_nouns(text)  
     
     # Apply grammatical corrections
     paraphrased_text = correct_article_errors(paraphrased_text)
     paraphrased_text = correct_singular_plural_errors(paraphrased_text)
     paraphrased_text = correct_tense_errors(paraphrased_text)
+    paraphrased_text = correct_double_negatives(paraphrased_text)
+    paraphrased_text = ensure_subject_verb_agreement(paraphrased_text)
 
     # Replace words with synonyms while maintaining verb form
     doc = nlp(paraphrased_text)