Create HGapp.py

HGapp.py

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+import gradio as gr
+from transformers import pipeline
+
+# Load the pre-trained question-answering model from Hugging Face
+qa_pipeline = pipeline("question-answering", model="distilbert-base-uncased-distilled-squad")
+
+# Define the function that takes inputs and returns the answer
+def answer_question(context, question):
+    result = qa_pipeline(question=question, context=context)
+    return result['answer']
+
+# Create the Gradio interface
+interface = gr.Interface(
+    fn=answer_question, 
+    inputs=[gr.inputs.Textbox(lines=7, label="Context (Enter the passage)"), gr.inputs.Textbox(lines=2, label="Question")],
+    outputs="text",
+    title="Question Answering Model",
+    description="Ask a question based on the given context.",
+)
+
+# Launch the interface
+interface.launch()