Added Q&A

app.py

@@ -200,17 +200,44 @@ QUESTION_ANSWER = f"""
         Answers to customer questions can be drawn from those documents.
         ⚡⚡ If you’d like to save inference time, you can first use <a href="https://huggingface.co/tasks/sentence-similarity">passage ranking</a> models to see which document might contain the answer to the question and iterate over that document with the QA model instead.
     </p>
+    <h2>Task Variants</h2>
+    <p>
+        There are different QA variants based on the inputs and outputs:
+        <ul>
+            <li><b>Extractive QA:</b> The model <b>extracts</b> the answer from a context. 
+            The context here could be a provided text, a table or even HTML! This is usually solved with BERT-like models.</li>
+            <li><b>Open Generative QA:</b> The model <b>generates</b> free text directly based on the context. 
+            You can learn more about the Text Generation task in its page.</li>
+            <li><b>Closed Generative QA:</b> In this case, no context is provided. The answer is completely generated by a model.</li>
+        </ul>
+        The schema above illustrates extractive, open book QA. The model takes a context and the question and extracts the answer from the given context.
+
+        You can also differentiate QA models depending on whether they are open-domain or closed-domain. 
+        Open-domain models are not restricted to a specific domain, while closed-domain models are restricted to a specific domain (e.g. legal, medical documents).
+    </p>
+    <h2>Inference</h2>
+    <p>
+    You can infer with QA models with the 🤗 Transformers library using the question-answering pipeline. 
+    If no model checkpoint is given, the pipeline will be initialized with distilbert-base-cased-distilled-squad. 
+    This pipeline takes a question and a context from which the answer will be extracted and returned.
+    </p>
+    <pre style="background: #f5f5f5; padding: 15px; border-radius: 5px; overflow-x: auto;">
+from transformers import pipeline
+
+qa_model = pipeline("question-answering")
+question = "Where do I live?"
+context = "My name is Merve and I live in İstanbul."
+qa_model(question = question, context = context)
+## {{'answer': 'İstanbul', 'end': 39, 'score': 0.953, 'start': 31}}
+    </pre>
 """
 
+SUMMARISATION = ""
+
 # ---- Placeholder HTML pages ----
 TEXT_GENERATION_HTML = TEXT_GENERATION
 
-QUESTION_ANSWER_HTML = """
-<div style="max-width: 800px; margin: auto;">
-    <h1>Question and Answer</h1>
-    <p>Q&A content goes here...</p>
-</div>
-"""
+QUESTION_ANSWER_HTML = QUESTION_ANSWER
 
 SUMMARISATION_HTML = """
 <div style="max-width: 800px; margin: auto;">