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app.py

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+import gradio as gr
+from transformers import pipeline
+
+# Load QA pipeline
+qa_pipeline = pipeline("question-answering", model="distilbert-base-uncased-distilled-squad")
+
+def answer_question(context, question):
+    result = qa_pipeline(question=question, context=context)
+    return result['answer']
+
+with gr.Blocks() as demo:
+    gr.Markdown("## 📘 Question Answering App (Extractive QA)")
+
+    context = gr.Textbox(
+        lines=8, 
+        placeholder="Paste a paragraph or article here...",
+        label="Context Paragraph"
+    )
+    question = gr.Textbox(
+        lines=2, 
+        placeholder="Type your question here...",
+        label="Question"
+    )
+    output = gr.Textbox(label="Answer")
+
+    btn = gr.Button("Get Answer")
+    btn.click(fn=answer_question, inputs=[context, question], outputs=output)
+
+demo.launch()

requirements.txt

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+gradio>=3.30
+transformers>=4.40
+torch>=1.13