Bert Q and A

app.py

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+import gradio as gr
+from transformers import pipeline
+
+print('starat')
+nlp_qa = pipeline(
+    'question-answering',
+    model='mrm8488/bert-italian-finedtuned-squadv1-it-alfa',
+    tokenizer='mrm8488/bert-italian-finedtuned-squadv1-it-alfa'
+)
+print('end')
+
+def start(question, context):
+    response = nlp_qa({
+        'question': question,
+        'context': context
+    })
+    return response['answer'], response['score']
+
+face = gr.Interface(
+    fn=start, 
+    inputs=[gr.inputs.Textbox(lines=1, placeholder="Question Here… "), gr.inputs.Textbox(lines=10, placeholder="Context Here… ")], 
+    outputs=["text", "number"]
+)
+face.launch(share=True)

requirements.txt

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+gradio
+torch
+transformers
+sentencepiece