| import gradio as gr | |
| from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline | |
| model_name = "bert-large-uncased-whole-word-masking-finetuned-squad" | |
| nlp = pipeline('question-answering', model=model_name, tokenizer=model_name) | |
| def chat(context, question): | |
| QA_input = { | |
| "question" : question, | |
| "context" : context | |
| } | |
| res = nlp(QA_input) | |
| return res['answer'] | |
| screen = gr.Interface( | |
| fn = chat, | |
| inputs = [gr.Textbox(lines = 8, placeholder = "Enter your context here 👉"), gr.Textbox(lines = 2, placeholder = "Enter your question here 👉")], | |
| outputs = gr.Textbox(lines = 15, placeholder = "Your answer will be here soon 🚀"), | |
| title="Facilitating the QnA with bert-large-uncased-whole-word-masking-finetuned-squad 👩🏻💻📓✍🏻💡", | |
| description="This app aims to facilitate the simple QnA with the provided context💡", | |
| theme="soft", | |
| article = """### Disclaimer : This model is purely used for QnA. User is expected to paste the text from which they want the answer in context section. <br>        Then paste the question in the question section. <br>        User will get the answer in the output section.""" | |
| ) | |
| screen.launch() |