Add Luhn Summarizer app with Gradio interface and required dependencies

app.py

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+from sumy.parsers.plaintext import PlaintextParser
+from sumy.nlp.tokenizers import Tokenizer
+from sumy.summarizers.luhn import LuhnSummarizer
+from sumy.nlp.stemmers import Stemmer
+from sumy.utils import get_stop_words
+import gradio as gr
+import nltk
+import time
+
+def luhn_summarizer(text_corpus):
+    start_time = time.time()
+    parser = PlaintextParser.from_string(text_corpus, Tokenizer("english"))
+    stemmer = Stemmer("english")
+    summarizer = LuhnSummarizer(stemmer)
+    summarizer.stop_words = get_stop_words("english")
+    sentences = summarizer(parser.document, 10)
+    summary = ""
+    for sentence in sentences:
+        summary += str(sentence) + ""
+    end_time = time.time()
+    print(f"Time taken: {end_time - start_time:.2f} seconds")
+    return summary
+
+def clear_everything(text_corpus, summary):
+    return None, None
+
+theme = gr.themes.Soft(
+    primary_hue="purple",
+    secondary_hue="cyan",
+    neutral_hue="slate",
+    font=[
+        gr.themes.GoogleFont('Syne'), 
+        gr.themes.GoogleFont('Poppins'), 
+        gr.themes.GoogleFont('Poppins'), 
+        gr.themes.GoogleFont('Poppins')
+    ],
+)
+
+with gr.Blocks(theme=theme, title="Luhn Summarizer", fill_height=True) as app:
+    gr.HTML(
+        value ='''
+        <h1 style="text-align: center;">Luhn Summarizer</h1>
+        <p style="text-align: center;">This app uses a Luhn approach to summarize PDF documents based on CPU.</p>
+        <p style="text-align: center;">The summarization process can take some time depending on the size of the text corpus and the complexity of the content.</p>
+        ''')
+    with gr.Row():
+        with gr.Column():
+            text_corpus = gr.TextArea(label="Text Corpus", placeholder="Paste the text corpus here", lines=5)
+            with gr.Row():
+                clear_btn = gr.Button(value="Clear", variant='stop')
+                summarize_btn = gr.Button(value="Summarize", variant='primary')
+        summary = gr.TextArea(label="Raw Data", placeholder="The generated raw data will be displayed here", lines=7, interactive=False, show_copy_button=True)
+
+    summarize_btn.click(
+        luhn_summarizer,
+        inputs=[text_corpus],
+        outputs=[summary],
+        concurrency_limit=25,
+        scroll_to_output=True,
+        show_api=True,
+        api_name="luhn_summarizer",
+        show_progress="full",
+    )
+    clear_btn.click(clear_everything, inputs=[text_corpus, summary], outputs=[text_corpus, summary], show_api=False)
+
+nltk.download('punkt', quiet=True)
+nltk.download('punkt_tab', quiet=True)
+app.queue(default_concurrency_limit=25).launch(show_api=True, max_threads=500, ssr_mode=False)

requirements.txt

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+gradio==5.8.0
+sumy==0.11.0
+nltk==3.9.1