Update

app.py

@@ -1,3 +1,39 @@
+import tensorflow as tf
+
+#!pip install transformers
+
+from transformers import pipeline
+
+# importing necessary libraries
+from transformers import AutoTokenizer, TFAutoModelForQuestionAnswering
+
+
+tokenizer = AutoTokenizer.from_pretrained("bert-large-uncased-whole-word-masking-finetuned-squad")
+model = TFAutoModelForQuestionAnswering.from_pretrained("bert-large-uncased-whole-word-masking-finetuned-squad",return_dict=False)
+
+nlp = pipeline("question-answering", model=model, tokenizer=tokenizer)
+
+#!pip install gradio
 import gradio as gr
 
-gr.Interface.load("models/abdalrahmanshahrour/questionanswering-v5").launch()
+# creating the function
+def func(context, question):
+  result = nlp(question = question, context=context)
+  return result['answer']
+
+example_1 = "(1) My name is Abdalrahman Shahrour, I am a data scientist and AI engineer"
+qst_1 =  "what is shahrour's profession?"
+
+example_2 = "(2) Natural Language Processing (NLP) allows machines to break down and interpret human language. It's at the core of tools we use every day – from translation software, chatbots, spam filters, and search engines, to grammar correction software, voice assistants, and social media monitoring tools."
+qst_2 =  "What is NLP used for?"
+
+# creating the interface
+app = gr.Interface(fn=func, inputs = ['textbox', 'text'], outputs = 'textbox', 
+                   title = 'Question Answering bot', theme = 'dark-grass',
+                   description = 'Input context and question, then get answers!',
+                   examples = [[example_1, qst_1],
+                               [example_2, qst_2]]
+                   )
+
+# launching the app
+app.launch(inline=False)