Update app.py

app.py

@@ -5,6 +5,8 @@ import spacy
 import subprocess
 import nltk
 from nltk.corpus import wordnet
+from grammarChecker.util import check_becauseError, check_butError, check_TenseError, check_articleError
+from pattern.en import conjugate, tenses
 
 # Initialize the English text classification pipeline for AI detection
 pipeline_en = pipeline(task="text-classification", model="Hello-SimpleAI/chatgpt-detector-roberta")
@@ -51,6 +53,45 @@ def capitalize_sentences_and_nouns(text):
 
     return ' '.join(corrected_text)
 
+# Function to check and correct tense consistency in sentences using Pattern.en
+def check_tense_consistency(text):
+    doc = nlp(text)
+    corrected_sentences = []
+
+    for sent in doc.sents:
+        verbs = [token for token in sent if token.pos_ == 'VERB']
+        if verbs:
+            # Find the most common tense in the sentence
+            common_tense = None
+            for verb in verbs:
+                verb_tense = tenses(verb.text)
+                if verb_tense:
+                    common_tense = verb_tense[0][0]
+                    break
+            
+            # Conjugate all verbs to the common tense if there's inconsistency
+            corrected_sentence = []
+            for token in sent:
+                if token.pos_ == 'VERB' and common_tense:
+                    corrected_verb = conjugate(token.text, tense=common_tense)
+                    corrected_sentence.append(corrected_verb)
+                else:
+                    corrected_sentence.append(token.text)
+            corrected_sentences.append(' '.join(corrected_sentence))
+        else:
+            corrected_sentences.append(sent.text)
+
+    return ' '.join(corrected_sentences)
+
+# Function to perform grammar and structure corrections using external grammar functions
+def grammar_correction(text):
+    error_count_because, corrected_text_because = check_becauseError(text)
+    error_count_but, corrected_text_but = check_butError(corrected_text_because)
+    error_count_tense, corrected_text_tense = check_TenseError(corrected_text_but)
+    error_count_article, final_corrected_text = check_articleError(corrected_text_tense)
+    
+    return final_corrected_text
+
 # Paraphrasing function using SpaCy and NLTK (Humanifier)
 def paraphrase_with_spacy_nltk(text):
     doc = nlp(text)
@@ -84,13 +125,19 @@ def paraphrase_with_spacy_nltk(text):
     
     return corrected_text
 
-# Combined function: Paraphrase -> Capitalization (Humanifier)
+# Combined function: Paraphrase -> Grammar Correction -> Capitalization -> Tense Consistency (Humanifier)
 def paraphrase_and_correct(text):
     # Step 1: Paraphrase the text
     paraphrased_text = paraphrase_with_spacy_nltk(text)
     
-    # Step 2: Capitalize sentences and proper nouns
-    final_text = capitalize_sentences_and_nouns(paraphrased_text)
+    # Step 2: Grammar and structure corrections
+    grammatically_corrected_text = grammar_correction(paraphrased_text)
+    
+    # Step 3: Capitalize sentences and proper nouns
+    capitalized_text = capitalize_sentences_and_nouns(grammatically_corrected_text)
+    
+    # Step 4: Check and correct tense consistency
+    final_text = check_tense_consistency(capitalized_text)
     
     return final_text
 
@@ -110,7 +157,7 @@ with gr.Blocks() as demo:
         paraphrase_button = gr.Button("Paraphrase & Correct")
         output_text = gr.Textbox(label="Paraphrased Text")
 
-        # Connect the paraphrasing function to the button
+        # Connect the paraphrasing and correction function to the button
         paraphrase_button.click(paraphrase_and_correct, inputs=text_input, outputs=output_text)
 
 # Launch the app with the remaining functionalities