Create app.py

app.py

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+import gradio as gr 
+from transformers import RobertaForQuestionAnswering
+from transformers import BertForQuestionAnswering
+from transformers import AutoTokenizer
+
+model1 = RobertaForQuestionAnswering.from_pretrained("pedramyazdipoor/persian_xlm_roberta_large")
+tokenizer1 = AutoTokenizer.from_pretrained("pedramyazdipoor/persian_xlm_roberta_large")
+
+roberta_large = pipeline(task='question-answering', model=model1, tokenizer=tokenizer1)
+
+def Q_A(contetx, question):
+  answer_pedram = roberta_large({"question":question, "context":context})['answer']
+  return answer_pedram
+
+
+
+# Create title, description and article strings
+title = "Question and answer based on Roberta model"
+description = "سیستم پردازش زبانی پرسش و پاسخ"
+article = "آموزش داده شده با مدل زبانی روبرتا"
+
+
+demo = gr.Interface(fn=Q_A, # mapping function from input to output
+                    inputs=[gr.Textbox(label='پرسش خوذ را وارد کنید:', show_label=True, text_align='right'),
+                            gr.Textbox(label='متن منبع خود را وارد کنید', show_label=True, text_align='right')], # what are the inputs?
+                    outputs=gr.Text(), # what are the outputs?
+                              # our fn has two outputs, therefore we have two outputs
+                    # Create examples list from "examples/" directory
+                    title=title,
+                    description=description,
+                    article=article)
+
+# Launch the demo!
+demo.launch(share=True)