import os
import gradio as gr
from transformers import pipeline
import spacy
import subprocess
import nltk
from nltk.corpus import wordnet
from spellchecker import SpellChecker
from flask import Flask, jsonify, request

# Initialize Flask app
app = Flask(__name__)

# Initialize the English text classification pipeline for AI detection
pipeline_en = pipeline(task="text-classification", model="Hello-SimpleAI/chatgpt-detector-roberta")

# 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")
except OSError:
    subprocess.run(["python", "-m", "spacy", "download", "en_core_web_sm"])
    nlp = spacy.load("en_core_web_sm")

# 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']

# Other processing functions (remove redundant words, capitalization, etc.) as previously defined
# For brevity, I'm skipping them here since they're unchanged. Make sure to include all the defined functions from the original code.

# Function to paraphrase and correct grammar with enhanced accuracy
def paraphrase_and_correct(text):
    cleaned_text = remove_redundant_words(text)
    paraphrased_text = capitalize_sentences_and_nouns(cleaned_text)
    paraphrased_text = force_first_letter_capital(paraphrased_text)
    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)
    paraphrased_text = rephrase_with_synonyms(paraphrased_text)
    paraphrased_text = correct_spelling(paraphrased_text)
    
    return paraphrased_text

# API Endpoint for AI Detection
@app.route('/api/ai-detection', methods=['POST'])
def ai_detection():
    data = request.get_json()
    text = data.get('text', '')
    
    if text:
        label, score = predict_en(text)
        return jsonify({"label": label, "score": score})
    else:
        return jsonify({"error": "No text provided"}), 400

# API Endpoint for Paraphrasing and Grammar Correction
@app.route('/api/paraphrase-correct', methods=['POST'])
def paraphrase_and_correct_api():
    data = request.get_json()
    text = data.get('text', '')
    
    if text:
        corrected_text = paraphrase_and_correct(text)
        return jsonify({"corrected_text": corrected_text})
    else:
        return jsonify({"error": "No text provided"}), 400

# Gradio app setup with two tabs
def launch_gradio():
    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(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=10, label='Corrected Text', placeholder="The corrected text will appear here...")

            # Connect the paraphrasing and correction function to the button
            button2.click(fn=paraphrase_and_correct, inputs=t2, outputs=result2)

    demo.launch(share=True)  # Share=True to create a public link

# Launch Gradio interface in a separate thread
if __name__ == '__main__':
    # Run Flask app in one thread and Gradio in another
    from threading import Thread

    # Gradio interface
    gradio_thread = Thread(target=launch_gradio)
    gradio_thread.start()

    # Flask API
    app.run(debug=True, port=5000)