Create app.py

Use the huggingface Transformers library for different tasks starting with a question and answering task.

app.py

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+from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
+import gradio as grad
+import ast
+
+# First, the RoBERTa base model is used, fine-tuned using the SQuAD 2.0 dataset. 
+# It’s been trained on question-answer pairs, including unanswerable questions, for the task of question and answering.
+mdl_name = "deepset/roberta-base-squad2"
+my_pipeline = pipeline('question-answering', model=mdl_name, tokenizer=mdl_name)
+
+def answer_question(question,context):
+    text= "{"+"'question': '"+question+"','context': '"+context+"'}"
+    di=ast.literal_eval(text)
+    response = my_pipeline(di)
+    return response
+
+grad.Interface(answer_question, inputs=["text","text"], outputs="text").launch()