import gradio as gr
from googletrans import Translator
import nltk
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer

# Download necessary NLTK data
nltk.download('punkt', quiet=True)
nltk.download('stopwords', quiet=True)
nltk.download('wordnet', quiet=True)

# Initialize components
translator = Translator()

def natural_language_understanding(text):
    tokens = word_tokenize(text.lower())
    stop_words = set(stopwords.words('english'))
    lemmatizer = WordNetLemmatizer()
    processed_tokens = [lemmatizer.lemmatize(token) for token in tokens if token not in stop_words]
    return " ".join(processed_tokens)

def translate_text(text, target_language):
    translated = translator.translate(text, dest=target_language)
    return translated.text

def process_input(input_text, feature, target_language):
    if not input_text:
        return "No input provided"

    processed_text = natural_language_understanding(input_text)

    if feature == "Translation":
        result = translate_text(processed_text, target_language)
    elif feature == "Transcription":
        result = processed_text
    else:
        result = "Invalid feature selected"

    return result

# Create Gradio interface
iface = gr.Interface(
    fn=process_input,
    inputs=[
        gr.Textbox(label="Input Text"),
        gr.Radio(["Translation", "Transcription"], label="Feature"),
        gr.Textbox(label="Target Language (for translation)")
    ],
    outputs=gr.Textbox(label="Result"),
    title="Simple Multi-Faceted Chatbot",
    description="Enter text, choose a feature, and specify a target language for translation if needed."
)

# Launch the interface
iface.launch()