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app.py

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+# -*- coding: utf-8 -*-
+"""Question_Answering_Gradio.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/12ga045iO8c2vMqYZQY4zPttnEv8dIUZe
+"""
+
+# Install Hugging Face Transformers
+!pip install transformers pipeline
+'''
+"Installs HuggingFace development version: Contains all required dependencies(PyTorch, TF,...)"
+!pip install transformers[sentencepiece]
+'''
+
+from transformers import pipeline
+
+model_checkpoint = "Madhana/distilroberta-base-finetuned-wikitext2-SQuAD-qa-WandB2"
+new_model = AutoModelForQuestionAnswering.from_pretrained(model_checkpoint)
+tokenizer = AutoTokenizer.from_pretrained("distilroberta-base", use_fast=True)
+qa = pipeline("question-answering", new_model, tokenizer, tokenizer)
+
+!pip install gradio
+
+import gradio as gr
+
+demo = gr.Blocks()
+
+with demo:
+    gr.Markdown("Language Model QA Demo")
+    with gr.Tabs():
+        with gr.TabItem("Question Answering"):
+            with gr.Row():
+                qa_input = gr.Textbox(label = "Input Text")
+                qa_context = gr.Textbox(label = "Input Context")
+                qa_output = gr.Textbox(label = "Output")
+            qa_button = gr.Button("Answer")
+
+    qa_button.click(qa, inputs=[qa_input, qa_context], outputs=qa_output)
+    
+
+demo.launch() # share=True

requirements.txt

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+# transformers
+transformers
+gradio
+torch
+scipy
+tqdm
+pyyaml
+ftfy
+pandas
+numpy