Update app.py

app.py

@@ -46,6 +46,7 @@ def plagiarism_removal(text):
         if word.lower() in stop_words or word.lower() in exclude_words or word in string.punctuation:
             return word
         
+        # Find synonyms
         synonyms = set()
         for syn in wordnet.synsets(word):
             for lemma in syn.lemmas():
@@ -53,7 +54,7 @@ def plagiarism_removal(text):
                     synonyms.add(lemma.name())
 
         pos_tag_word = nltk.pos_tag([word])[0]
-        
+
         if pos_tag_word[1] in exclude_tags:
             return word
         
@@ -80,37 +81,6 @@ def plagiarism_removal(text):
 
     return " ".join(corrected_text)
 
-def change_voice(text):
-    doc = nlp(text)
-    sentences = list(doc.sents)
-    modified_sentences = []
-
-    for sent in sentences:
-        # Randomly choose to convert to active or passive voice
-        if random.choice([True, False]):  # Change voice
-            subject = None
-            verb = None
-            obj = None
-            
-            for token in sent:
-                if token.dep_ == 'nsubj':  # Subject
-                    subject = token
-                elif token.dep_ == 'ROOT':  # Main verb
-                    verb = token
-                elif token.dep_ == 'dobj':  # Object
-                    obj = token
-
-            if subject and verb and obj:
-                # Active to Passive
-                passive_sentence = f"{obj.text} was {verb.lemma_} by {subject.text}"
-                modified_sentences.append(passive_sentence)
-            else:
-                modified_sentences.append(sent.text)  # No change if structure doesn't match
-        else:
-            modified_sentences.append(sent.text)  # No change
-
-    return ' '.join(modified_sentences)
-
 def predict_en(text):
     res = pipeline_en(text)[0]
     return res['label'], res['score']
@@ -219,24 +189,41 @@ def correct_spelling(text):
             corrected_words.append(word)
     return ' '.join(corrected_words)
 
-# Main function for paraphrasing and grammar correction
 def paraphrase_and_correct(text):
-    # Change narrative voice randomly
-    voice_changed_text = change_voice(text)
-    
-    cleaned_text = remove_redundant_words(voice_changed_text)
-    plag_removed = plagiarism_removal(cleaned_text)
-    paraphrased_text = capitalize_sentences_and_nouns(plag_removed)
-    paraphrased_text = force_first_letter_capital(paraphrased_text)
-    paraphrased_text = correct_article_errors(paraphrased_text)
-    paraphrased_text = correct_tense_errors(paraphrased_text)
-    paraphrased_text = ensure_subject_verb_agreement(paraphrased_text)
-    paraphrased_text = fix_possessives(paraphrased_text)
-    paraphrased_text = fix_punctuation_spacing(paraphrased_text)
-    paraphrased_text = correct_spelling(paraphrased_text)
-    
-    return paraphrased_text
-
-# Gradio interface setup
-iface = gr.Interface(fn=paraphrase_and_correct, inputs="text", outputs="text", title="Text Paraphraser and Corrector", description="A tool to paraphrase and correct grammar in your text.")
-iface.launch()
+    paragraphs = text.split("\n\n")  # Split by paragraphs
+
+    # Process each paragraph separately
+    processed_paragraphs = []
+    for paragraph in paragraphs:
+        cleaned_text = remove_redundant_words(paragraph)
+        plag_removed = plagiarism_removal(cleaned_text)
+        paraphrased_text = capitalize_sentences_and_nouns(plag_removed)
+        paraphrased_text = force_first_letter_capital(paraphrased_text)
+        paraphrased_text = correct_article_errors(paraphrased_text)
+        paraphrased_text = correct_tense_errors(paraphrased_text)
+        paraphrased_text = ensure_subject_verb_agreement(paraphrased_text)
+        paraphrased_text = fix_possessives(paraphrased_text)
+        paraphrased_text = correct_spelling(paraphrased_text)
+        paraphrased_text = fix_punctuation_spacing(paraphrased_text)
+        processed_paragraphs.append(paraphrased_text)
+
+    return "\n\n".join(processed_paragraphs)  # Reassemble the text with paragraphs
+
+# Gradio app setup
+with gr.Blocks() as demo:
+    with gr.Tab("AI Detection"):
+        t1 = gr.Textbox(lines=5, label='Text')
+        button1 = gr.Button("🤖 Predict!")
+        label1 = gr.Textbox(lines=1, label='Predicted Label 🎃')
+        score1 = gr.Textbox(lines=1, label='Prob')
+
+        button1.click(fn=predict_en, inputs=t1, outputs=[label1, score1])
+
+    with gr.Tab("Paraphrasing & Grammar Correction"):
+        t2 = gr.Textbox(lines=5, label='Enter text for paraphrasing and grammar correction')
+        button2 = gr.Button("🔄 Paraphrase and Correct")
+        result2 = gr.Textbox(lines=5, label='Corrected Text')
+
+        button2.click(fn=paraphrase_and_correct, inputs=t2, outputs=result2)
+
+demo.launch(share=True)