Create app.py

app.py

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+# 📝 Document Q&A Demo | CPU-only HF Space
+
+import gradio as gr
+from transformers import pipeline
+
+# Load a fast, accurate QA model
+qa_pipeline = pipeline(
+    "question-answering",
+    model="deepset/roberta-base-squad2",
+    device=-1     # force CPU
+)
+
+def answer_question(context: str, question: str):
+    if not context.strip() or not question.strip():
+        return "Please provide both a document context and a question."
+    result = qa_pipeline(question=question, context=context)
+    answer = result["answer"]
+    score  = round(result["score"], 3)
+    return f"{answer}  (confidence: {score})"
+
+with gr.Blocks(title="📝 Document Q&A") as demo:
+    gr.Markdown(
+        "# 📝 Document Q&A\n"
+        "Paste in any text (policy, FAQ, product manual), ask a question, and get an instant answer."
+    )
+    with gr.Row():
+        ctx = gr.Textbox(lines=10, placeholder="Paste your document text here...", label="Document Context")
+        qry = gr.Textbox(lines=2, placeholder="Ask a question about the text above...", label="Question")
+    btn    = gr.Button("Get Answer 🔍", variant="primary")
+    out    = gr.Textbox(label="Answer", interactive=False)
+
+    btn.click(answer_question, [ctx, qry], out)
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0")