Create app.py

app.py

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+import gradio as gr
+
+from transformers import pipeline
+
+
+
+
+# Load a pre-trained question answering pipeline
+
+question_answerer = pipeline("question-answering", model="bert-large-uncased-whole-word-masking-finetuned-squad")
+
+
+
+
+def answer_question(context, question):
+
+    """
+
+    Takes context and a question as input and returns the predicted answer.
+
+    """
+
+    if context and question:
+
+        result = question_answerer(question=question, context=context)
+
+        answer = result['answer']
+
+        confidence = f"{result['score']:.4f}"
+
+        return f"Answer: {answer}", f"Confidence: {confidence}"
+
+    else:
+
+        return "Please provide both context and a question.", ""
+
+
+
+
+# Define the Gradio interface
+
+iface = gr.Interface(
+
+    fn=answer_question,
+
+    inputs=[
+
+        gr.Textbox(lines=7, placeholder="Enter the context here..."),
+
+        gr.Textbox(placeholder="Ask a question about the context...")
+
+    ],
+
+    outputs=[
+
+        gr.Textbox(label="Predicted Answer"),
+
+        gr.Textbox(label="Confidence Score")
+
+    ],
+
+    title="Simple Question Answering",
+
+    description="Enter a block of text (context) and then ask a question about it. The app will try to find the answer within the text.",
+
+)
+
+
+
+
+# Launch the Gradio app
+
+iface.launch()