Add Gradio app for article summarization

app.py

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+import gradio as gr
+from article_utils import extract_article_text, summarize_article
+from qa_utils import answer_question
+
+def summarize_from_url(url):
+    try:
+        text = extract_article_text(url)
+        summary = summarize_article(text)
+        return summary
+    except Exception as e:
+        return f"Error: {e}"
+
+demo = gr.Interface(fn=summarize_from_url, inputs="text", outputs="text", title="Article Summarizer")
+
+if __name__ == "__main__":
+    demo.launch()

article_utils.py

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+from newspaper import Article
+from transformers import pipeline
+
+article_summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6") # Replace with your actual token
+
+def extract_article_text(url):
+    article = Article(url)
+    article.download()
+    article.parse()
+    return article.text
+
+def summarize_article(text):
+    summary = article_summarizer(text, max_length=150, min_length=50, do_sample=False)
+    return summary[0]['summary_text']

qa_utils.py

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+from transformers import pipeline
+import os
+
+# Set your Hugging Face token as an environment variable
+# Make sure this token is valid and has the necessary permissions
+# or, pass the token directly to the pipeline function
+qa_pipeline = pipeline("question-answering", model="distilbert-base-cased-distilled-squad") # Replace with your actual token
+
+
+def answer_question(question, context):
+    result = qa_pipeline(question=question, context=context)
+    return result['answer']

requirements.txt

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+# requirements.txt
+%%writefile requirements.txt
+gradio
+newspaper3k
+transformers
+torch