app.py

import gradio as gr
from gensim.models import TfidfModel
from gensim.corpora import Dictionary
from gensim.utils import simple_preprocess
from gensim.parsing.preprocessing import remove_stopwords
import numpy as np
import warnings
warnings.filterwarnings('ignore')

# Example texts
EXAMPLES = {
    "Scientific Abstract": """
    Compatibility of systems of linear constraints over the set of natural numbers. 
    Criteria of compatibility of a system of linear Diophantine equations, strict inequations, 
    and nonstrict inequations are considered. Upper bounds for components of a minimal set of solutions 
    and algorithms of construction of minimal generating sets of solutions for all types of systems are given.
    """,
    "News Article": """
    Machine learning is revolutionizing the way we interact with technology. 
    Artificial intelligence systems are becoming more sophisticated, enabling automated decision making 
    and pattern recognition at unprecedented scales. Deep learning algorithms continue to improve, 
    making breakthroughs in natural language processing and computer vision.
    """,
    "Technical Documentation": """
    The user interface provides intuitive navigation through contextual menus and adaptive layouts. 
    System responses are optimized for performance while maintaining high reliability standards. 
    Database connections are pooled to minimize resource overhead and maximize throughput.
    """
}

def preprocess_text(text):
    # Remove stopwords
    text = remove_stopwords(text)
    # Tokenize and clean text
    tokens = simple_preprocess(text, deacc=True)
    return ' '.join(tokens)

# Initialize text processing components
def extract_keywords(text, num_keywords=10, scores=True, min_length=1):
    # Preprocess text
    processed_text = remove_stopwords(text.lower())
    tokens = simple_preprocess(processed_text, deacc=True)
    
    # Create dictionary and corpus
    dictionary = Dictionary([tokens])
    corpus = [dictionary.doc2bow(tokens)]
    
    # Create TF-IDF model
    tfidf = TfidfModel(corpus)
    tfidf_corpus = tfidf[corpus][0]
    
    # Sort by scores
    sorted_keywords = sorted(tfidf_corpus, key=lambda x: x[1], reverse=True)
    
    # Get top keywords and filter by length
    results = []
    for word_id, score in sorted_keywords:
        word = dictionary[word_id]
        if len(word.split()) >= min_length:
            if scores:
                results.append(f"• {word:<30} (score: {score:.4f})")
            else:
                results.append(f"• {word}")
        if len(results) >= num_keywords:
            break
            
    return "\n".join(results) if results else "No keywords found."

# Update the interface click handler to match the function parameters
extract_btn.click(
    extract_keywords,
    inputs=[input_text, num_keywords, show_scores, min_length],
    outputs=[output_text]
)

def load_example(example_name):
    return EXAMPLES.get(example_name, "")

# Create Gradio interface
with gr.Blocks(title="Gensim Keyword Extraction") as demo:
    gr.Markdown("# 📑 Gensim Keyword Extraction")
    gr.Markdown("Extract keywords using Gensim's text processing capabilities")
    
    with gr.Row():
        with gr.Column(scale=2):
            input_text = gr.Textbox(
                label="Input Text",
                placeholder="Enter your text here...",
                lines=8
            )
            example_dropdown = gr.Dropdown(
                choices=list(EXAMPLES.keys()),
                label="Load Example Text"
            )
        
        with gr.Column(scale=1):
            ratio = gr.Slider(
                minimum=1,
                maximum=100,
                value=20,
                step=1,
                label="Keyword Ratio (%)"
            )
            
            min_length = gr.Slider(
                minimum=1,
                maximum=5,
                value=1,
                step=1,
                label="Minimum Words per Keyword"
            )
            
            show_scores = gr.Checkbox(
                label="Show Relevance Scores",
                value=True
            )
            
            extract_btn = gr.Button(
                "Extract Keywords", 
                variant="primary"
            )
    
    output_text = gr.Textbox(
        label="Extracted Keywords",
        lines=10,
        interactive=False
    )
    
    # Set up event handlers
    example_dropdown.change(
        load_example,
        inputs=[example_dropdown],
        outputs=[input_text]
    )
    
    extract_btn.click(
        extract_keywords,
        inputs=[
            input_text,
            ratio,
            show_scores,
            min_length
        ],
        outputs=[output_text]
    )

demo.launch()