import re
from textblob import TextBlob
import nltk
import emopy
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import torch
import spacy
from flask import Flask, request, render_template

nltk.download('averaged_perceptron_tagger')
nltk.download('punkt')
nltk.download('maxent_ne_chunker')
nltk.download('words')

nlp = spacy.load("en_core_web_sm")

app = Flask(__name__)

@app.route('/', methods=['GET', 'POST'])
def index():
    if request.method == 'POST':
        text = request.form['text']
        paraphrase_option = request.form['paraphrase_option']
        remove_special_chars = request.form.get('remove_special_chars')
        summarize = request.form.get('summarize')
        correct_grammar = request.form.get('correct_grammar')

        if correct_grammar:
            text = str(TextBlob(text).correct())

        if remove_special_chars:
            text = re.sub(r'[^\w\s]', '', text)

        if summarize:
            doc = nlp(text)
            sentences = [sent.text for sent in doc.sents]
            text = " ".join(sentences[:3]) + "..."

        if paraphrase_option == 'repeat':
            text = re.sub(r'\b(\w+)\b', r'\1', text)
        elif paraphrase_option == 'emotion_detector':
            emotion = emopy.EmotionDetector()
            emotions = emotion.detect_emotion(text)
            emotion_labels = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral']
            index = np.argmax(emotions)
            emotion = emotion_labels[index]
            if emotion == 'happy':
                text = text.upper()
            elif emotion == 'sad':
                text = text.lower()
            else:
                text = text.capitalize()

        return render_template('index.html', text=text)
    return render_template('index.html')

if __name__ == '__main__':
    app.run(host="0.0.0.0",port=7860,debug=True)