Upload app.py

app.py

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+from transformers import AutoModelForQuestionAnswering,AutoTokenizer,pipeline
+import gradio as gr
+
+model = AutoModelForQuestionAnswering.from_pretrained('uer/roberta-base-chinese-extractive-qa')
+tokenizer = AutoTokenizer.from_pretrained('uer/roberta-base-chinese-extractive-qa')
+QA = pipeline('question-answering', model=model, tokenizer=tokenizer)
+
+
+
+
+def get_out(text1,text2):
+
+    QA_input={'question':text1,'context':text2}
+
+
+    res=QA(QA_input)
+    # res['answer']
+
+    return  res['answer']
+
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        question = gr.Textbox(label='question')
+        greet_btn = gr.Button('compute')
+    context=gr.Textbox(label='context')
+    res=gr.Textbox(label='result')
+    greet_btn.click(fn=get_out,inputs=[question,context],outputs=res)
+
+demo.launch(server_port=9090)
+
+
+