Update app.py

app.py

@@ -9,6 +9,23 @@ def predict(context, question):
     return res["answer"]
 
 md = """
+### Description
+
+In this project work we build a **Text Extraction Question-Answering system** using **BERT** model. QA system is a 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.
+
+
+### Design of the system:
+<br>
+<div style="text-align: center;">
+    <img src="https://i.imgur.com/G4qgMhE.jpeg" alt="Description Image" style="border: 2px solid #000; border-radius: 5px; width: 600px; height: auto; display: block; margin: 0 auto;">
+</div>
+
+### QA Application:
+Add a context paragraphs upto 512 tokens and ask a question based on the context. The model acccurately fetches the answer from the context in the form of a text span and display it.
+
 """
 
 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..."
@@ -21,6 +38,6 @@ gr.Interface(
         gr.Textbox(lines=2, value=question, label="Question"),
     ],
     outputs=gr.Textbox(label="Answer"),
-    title="Question Answering System",
+    title="Question & Answering with BERT using the SQuAD 2 dataset",
     description=md,
 ).launch()