"~"

app.py

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+# -*- coding: utf-8 -*-
+from transformers import pipeline
+import gradio as gr
+from datasets import load_dataset
+
+dataset = load_dataset("xjlulu/ntu_adl_QA", "Preprocess", split="validation")
+
+inputs = [
+    gr.inputs.Textbox(lines=3, label="Question"),
+    gr.inputs.Textbox(lines=12, label="Context paragraph"),
+]
+
+examples = [{"Question": data["question"], "Context paragraph": data["context"]} for data in dataset[2:6]]
+
+model_name = "cluecorpussmall"
+question_answering = pipeline("question-answering", model=f"xjlulu/ntu_adl_span_selection_{model_name}")
+
+def generate_answer(question, context):
+        result = question_answering(question=question, context=context)
+        return result['answer']
+
+iface = gr.Interface(
+    fn=generate_answer,
+    inputs=inputs,
+    outputs=gr.Textbox(lines=10, label="Answer"),
+    title="Question Answering",
+    description="Answer questions based on a given context paragraph",
+    live=False,
+    examples=examples,
+    share=True,
+    theme="huggingface"
+)
+
+iface.launch()

requirements.txt

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+text-generation==0.6.0
+transformers==4.31.0
+pymongo==4.4.1
+torch
+sentencepiece
+protobuf