from nltk.tokenize import sent_tokenize
from language_tool_python import LanguageTool
from nltk.sentiment import SentimentIntensityAnalyzer
import gradio as gr
import nltk
nltk.download('vader_lexicon')

# Initialize LanguageTool object outside the function
tool = LanguageTool('en-US')
sia = SentimentIntensityAnalyzer()

def grammar_check(text):
    matches = tool.check(text)
    corrected_sentences = tool.correct(text)
    return corrected_sentences

def analyze_sentiment(text):
    sentiment_scores = sia.polarity_scores(text)

    # Positive sentiment: score > 0
    # Neutral sentiment: score = 0
    # Negative sentiment: score < 0
    sentiment = ""
    if sentiment_scores['compound'] > 0:
        sentiment = "Positive"
    elif sentiment_scores['compound'] == 0:
        sentiment = "Neutral"
    else:
        sentiment = "Negative"

    return sentiment

def sentence_generator(text):
    sentences = sent_tokenize(text)
    for sentence in sentences:
        yield sentence

def CsharpGrammarly(text):
    corrected_sentences = grammar_check(text)
    sentiment_result = analyze_sentiment(corrected_sentences)
    total_words = len(text.split())
    return corrected_sentences, sentiment_result, total_words

iface = gr.Interface(
    fn=CsharpGrammarly,
    inputs=gr.inputs.Textbox(placeholder="Enter your text here..."),
    outputs=[
        gr.outputs.Textbox(label="Modified Grammar"),
        gr.outputs.Textbox(label="Sentiment Analysis"),
        gr.outputs.Textbox(label="Total Words Count")
    ],
    title="CSharpGrammarly",
    description="Correct spelling, grammar, and analyze sentiment."
)

iface.launch()