import nltk
from collections import Counter
import gradio as gr

# Download the necessary NLTK resources
nltk.download('punkt')
nltk.download('averaged_perceptron_tagger')

# Define the function for extracting the top 2 nouns
def extract_top_nouns(text):
    # Tokenize the text into individual words
    words = nltk.word_tokenize(text)

    # Tag each word with its part of speech
    tagged_words = nltk.pos_tag(words)

    # Extract only the nouns
    nouns = [word for word, tag in tagged_words if tag in ['NN', 'NNS', 'NNP', 'NNPS']]

    # Count the frequency of each noun
    noun_counts = Counter(nouns)

    # Get the top 2 most common nouns
    top_nouns = noun_counts.most_common(2)

    # Return the top 2 nouns as a string
    return ', '.join([noun[0] for noun in top_nouns])

# Define the Gradio interface
iface = gr.Interface(fn=extract_top_nouns, 
                     inputs=gr.Textbox(lines=2, placeholder='Enter text here...'), 
                     outputs='text')

# Launch the Gradio app
iface.launch(share=True)