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-import os
-import gradio as gr
-from transformers import pipeline
-import spacy
-import subprocess
-import nltk
-from nltk.corpus import wordnet
-
-# Initialize the English text classification pipeline for AI detection
-pipeline_en = pipeline(task="text-classification", model="Hello-SimpleAI/chatgpt-detector-roberta")
-
-# Function to predict the label and score for English text (AI Detection)
-def predict_en(text):
-    res = pipeline_en(text)[0]
-    return res['label'], res['score']
-
-# Ensure necessary NLTK data is downloaded for Humanifier
-nltk.download('wordnet')
-nltk.download('omw-1.4')
-
-# Ensure the SpaCy model is installed for Humanifier
-try:
-    nlp = spacy.load("en_core_web_sm")
-except OSError:
-    subprocess.run(["python", "-m", "spacy", "download", "en_core_web_sm"])
-    nlp = spacy.load("en_core_web_sm")
-
-# Grammar, Tense, and Singular/Plural Correction Functions
-
-# Correct article errors (e.g., "a apple" -> "an apple")
-def check_article_error(text):
-    tokens = nltk.pos_tag(nltk.word_tokenize(text))
-    corrected_tokens = []
-    
-    for i, token in enumerate(tokens):
-        word, pos = token
-        if word.lower() == 'a' and i < len(tokens) - 1 and tokens[i + 1][1] == 'NN':
-            corrected_tokens.append('an' if tokens[i + 1][0][0] in 'aeiou' else 'a')
-        else:
-            corrected_tokens.append(word)
-    
-    return ' '.join(corrected_tokens)
-
-# Correct tense errors (e.g., "She has go out" -> "She has gone out")
-def check_tense_error(text):
-    tokens = nltk.pos_tag(nltk.word_tokenize(text))
-    corrected_tokens = []
-    
-    for word, pos in tokens:
-        if word == "go" and pos == "VB":
-            corrected_tokens.append("gone")
-        elif word == "know" and pos == "VB":
-            corrected_tokens.append("known")
-        else:
-            corrected_tokens.append(word)
-    
-    return ' '.join(corrected_tokens)
-
-# Correct singular/plural errors (e.g., "There are many chocolate" -> "There are many chocolates")
-def check_pluralization_error(text):
-    tokens = nltk.pos_tag(nltk.word_tokenize(text))
-    corrected_tokens = []
-    
-    for word, pos in tokens:
-        if word == "chocolate" and pos == "NN":
-            corrected_tokens.append("chocolates")
-        elif word == "kids" and pos == "NNS":
-            corrected_tokens.append("kid")
-        else:
-            corrected_tokens.append(word)
-    
-    return ' '.join(corrected_tokens)
-
-# Combined function to correct grammar, tense, and singular/plural errors
-def correct_grammar_tense_plural(text):
-    text = check_article_error(text)
-    text = check_tense_error(text)
-    text = check_pluralization_error(text)
-    return text
-
-# Gradio app setup with three tabs
-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')
-
-        # Connect the prediction function to the button
-        button1.click(predict_en, inputs=[t1], outputs=[label1, score1], api_name='predict_en')
-    
-    with gr.Tab("Humanifier"):
-        text_input = gr.Textbox(lines=5, label="Input Text")
-        paraphrase_button = gr.Button("Paraphrase & Correct")
-        output_text = gr.Textbox(label="Paraphrased Text")
-
-        # Connect the paraphrasing function to the button
-        paraphrase_button.click(paraphrase_and_correct, inputs=text_input, outputs=output_text)
-    
-    with gr.Tab("Grammar Correction"):
-        grammar_input = gr.Textbox(lines=5, label="Input Text")
-        grammar_button = gr.Button("Correct Grammar")
-        grammar_output = gr.Textbox(label="Corrected Text")
-
-        # Connect the custom grammar, tense, and plural correction function to the button
-        grammar_button.click(correct_grammar_tense_plural, inputs=grammar_input, outputs=grammar_output)
-
-# Launch the app with all functionalities
-demo.launch()