Adding gradio interface

app.py

@@ -1,7 +1,28 @@
 import gradio as gr
+import tensorflow as tf
+import gradio as gr
+from transformers import pipeline, AutoTokenizer, TFAutoModelForQuestionAnswering
+
+#Option 1: Load the tokenizer and model separately
+#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)
+
+#Option 2: Use the HuggingFace pipeline function
+nlp = pipeline("question-answering", model=model, tokenizer=tokenizer)
 
-def greet(name):
-    return "Hello " + name + "!!"
+def func(context, question):
+    result = nlp(question=question, context=context)
+    return result['answer']
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+app = gr.Interface(fn=func,
+                   inputs = ['textbox', 'text'],
+                   outputs = gr.Textbox(lines=10),
+                   title = 'Question Answering Bot',
+                   description = 'Input context and question, then get answers!',
+                   examples = [[example_1, qst_1],
+                               [example_2, qst_2]],
+                   theme = "darkhuggingface",
+                   timeout = 120,
+                   allow_flagging="manual",
+                   flagging_options=["incorrect", "ambiguous", "offensive", "other"],
+                   ).queue()