Upload app.py

app.py

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+import gradio as gr
+from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
+
+model_name = "IProject-10/roberta-base-finetuned-squad2"
+nlp = pipeline("question-answering", model=model_name, tokenizer=model_name)
+
+def predict(context, question):
+    res = nlp({"question": question, "context": context})
+    return res["answer"]
+
+md = """In this project work we build a Text Retrieval Question-Answering system using BERT model. QA system is an important NLP task in which the user asks a question in natural language to the model as input and the model provides the answer in natural language as output.
+The language representation model BERT stands for Bidirectional Encoder Representations from Transformers. The model is based on the Devlin et al. paper: [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805).
+Dataset used is SQuAD 2.0 [Stanford Question Answering Dataset 2.0](https://rajpurkar.github.io/SQuAD-explorer/). It is a reading comprehension dataset which consists of question-answer pairs derived from Wikipedia articles written by crowdworkers. The answer to all the questions is in the form of a span of text.
+"""
+
+context = "The Amazon rainforest, also known in English as Amazonia or the Amazon Jungle, is a moist broadleaf forest that covers most of the Amazon basin of South America..."
+question = "Which continent is the Amazon rainforest in?"
+
+apple_context = "An apple is an edible fruit produced by an apple tree (Malus domestica)..."
+apple_question = "How many years have apples been grown for?"
+
+gr.Interface(
+    predict,
+    inputs=[
+        gr.Textbox(lines=7, value=context, label="Context Paragraph"),
+        gr.Textbox(lines=2, value=question, label="Question"),
+    ],
+    outputs=gr.Textbox(label="Answer"),
+    examples=[[apple_context, apple_question]],
+    title="Question & Answering with BERT using the SQuAD 2 dataset",
+    description=md,
+).launch()