Create app.py

app.py

@@ -0,0 +1,34 @@
+import gradio as gr
+from transformers import pipeline
+
+# Load the question-answering pipeline
+qa_pipeline = pipeline("question-answering", model="deepset/roberta-base-squad2")
+
+# Define the function for question answering
+def answer_question(context, question):
+    if not context.strip() or not question.strip():
+        return "Please provide both a context and a question."
+    try:
+        result = qa_pipeline(question=question, context=context)
+        return result["answer"]
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+# Create the Gradio interface
+demo = gr.Interface(
+    fn=answer_question,
+    inputs=[
+        gr.Textbox(lines=10, label="Context", placeholder="Enter the context here..."),
+        gr.Textbox(label="Question", placeholder="Enter the question here...")
+    ],
+    outputs=gr.Textbox(label="Answer"),
+    title="Question Answering with RoBERTa",
+    description=(
+        "Ask questions based on a given context using the `deepset/roberta-base-squad2` model. "
+        "Enter a passage of text as the context and a specific question about it to get an answer."
+    )
+)
+
+# Launch the app
+if __name__ == "__main__":
+    demo.launch()