Update app.py

app.py

@@ -4,32 +4,22 @@ from transformers import pipeline
 import spacy
 import subprocess
 import nltk
-from nltk.corpus import wordnet, stopwords  # Import stopwords here
+from nltk.corpus import wordnet
 from spellchecker import SpellChecker
 import re
-import random
-import string
 
-# Ensure necessary NLTK data is downloaded
-def download_nltk_resources():
-    try:
-        nltk.download('punkt')  # Tokenizer for English text
-        nltk.download('stopwords')  # Stop words
-        nltk.download('averaged_perceptron_tagger')  # POS tagger
-        nltk.download('wordnet')  # WordNet
-        nltk.download('omw-1.4')  # Open Multilingual Wordnet
-    except Exception as e:
-        print(f"Error downloading NLTK resources: {e}")
-
-# Call the download function
-download_nltk_resources()
 
+
+nltk.download('punkt')        
+nltk.download('stopwords')    
+nltk.download('averaged_perceptron_tagger')  
+nltk.download('wordnet')      
 top_words = set(stopwords.words("english"))  # More efficient as a set
 
 def plagiarism_removal(text):
     def plagiarism_remover(word):
         # Handle stopwords, punctuation, and excluded words
-        if word.lower() in top_words or word.lower() in exclude_words or word in string.punctuation:
+        if word.lower() in stop_words or word.lower() in exclude_words or word in string.punctuation:
             return word
         
         # Find synonyms
@@ -62,7 +52,7 @@ def plagiarism_removal(text):
         return synonym_choice
 
     # Tokenize, replace words, and join them back
-    para_split = nltk.word_tokenize(text)
+    para_split = word_tokenize(text)
     final_text = [plagiarism_remover(word) for word in para_split]
     
     # Handle spacing around punctuation correctly
@@ -75,6 +65,12 @@ def plagiarism_removal(text):
 
     return " ".join(corrected_text)
 
+
+
+
+
+
+
 # Words we don't want to replace
 exclude_tags = {'PRP', 'PRP$', 'MD', 'VBZ', 'VBP', 'VBD', 'VBG', 'VBN', 'TO', 'IN', 'DT', 'CC'}
 exclude_words = {'is', 'am', 'are', 'was', 'were', 'have', 'has', 'do', 'does', 'did', 'will', 'shall', 'should', 'would', 'could', 'can', 'may', 'might'}
@@ -85,6 +81,10 @@ pipeline_en = pipeline(task="text-classification", model="Hello-SimpleAI/chatgpt
 # Initialize the spell checker
 spell = SpellChecker()
 
+# Ensure necessary NLTK data is downloaded
+nltk.download('wordnet')
+nltk.download('omw-1.4')
+
 # Ensure the SpaCy model is installed
 try:
     nlp = spacy.load("en_core_web_sm")
@@ -205,37 +205,46 @@ def correct_spelling(text):
     corrected_words = []
     for word in words:
         corrected_word = spell.correction(word)
-        corrected_words.append(corrected_word)
+        if corrected_word is not None:
+            corrected_words.append(corrected_word)
+        else:
+            corrected_words.append(word)
     return ' '.join(corrected_words)
 
-# Main processing function for paraphrasing and grammar correction
+
+
+
+# Main function for paraphrasing and grammar correction
 def paraphrase_and_correct(text):
+     # Add synonym replacement here
     cleaned_text = remove_redundant_words(text)
-    cleaned_text = fix_punctuation_spacing(cleaned_text)
-    cleaned_text = fix_possessives(cleaned_text)
-    cleaned_text = capitalize_sentences_and_nouns(cleaned_text)
-    cleaned_text = force_first_letter_capital(cleaned_text)
-    cleaned_text = correct_tense_errors(cleaned_text)
-    cleaned_text = correct_article_errors(cleaned_text)
-    cleaned_text = ensure_subject_verb_agreement(cleaned_text)
-    cleaned_text = correct_spelling(cleaned_text)
-    plag_removed = plagiarism_removal(cleaned_text)
-    return plag_removed
-
-# Create the Gradio interface
+    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)
+
+    return paraphrased_text
+
+# Gradio app setup
 with gr.Blocks() as demo:
-    gr.Markdown("# AI Text Processor")
     with gr.Tab("AI Detection"):
-        t1 = gr.Textbox(lines=5, label='Input Text')
-        output1 = gr.Label()
-        button1 = gr.Button("🚀 Process!")
-        button1.click(fn=predict_en, inputs=t1, outputs=output1)
+        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 and Grammar Correction"):
-        t2 = gr.Textbox(lines=5, label='Input Text')
-        button2 = gr.Button("🚀 Process!")
-        output2 = gr.Textbox(lines=5, label='Processed Text')
+    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=output2)
+        button2.click(fn=paraphrase_and_correct, inputs=t2, outputs=result2)
 
-demo.launch()
+demo.launch(share=True)