add trans qa

app.py

@@ -1,4 +1,36 @@
 import gradio as gr
+
+from transformers import pipeline
+
+question_answerer = pipeline("question-answering")
+
+context = r"""
+Extractive Question Answering is the task of extracting an answer from a text given a question. An example of a
+question answering dataset is the SQuAD dataset, which is entirely based on that task. If you would like to fine-tune
+a model on a SQuAD task, you may leverage the examples/pytorch/question-answering/run_squad.py script.
+"""
+
+result = question_answerer(question="What is extractive question answering?", context=context)
+print(f"Answer: '{result['answer']}', score: {round(result['score'], 4)}, start: {result['start']}, end: {result['end']}")
+
+#result = question_answerer(question="What is a good example of a question answering dataset?", context=context)
+#print(f"Answer: '{result['answer']}', score: {round(result['score'], 4)}, start: {result['start']}, end: {result['end']}")
+
+def qa_func(paragraph, question):
+    #return model.predict(paragraph, question)["answer"]
+    return result['answer']
+
+iface = gr.Interface(qa_func, 
+    [
+        gr.inputs.Textbox(lines=7, label="Context", default=context), 
+        gr.inputs.Textbox(lines=1, label="Question", default="What is extractive question answering?"), 
+    ], 
+    gr.outputs.Textbox(label="Answer"))
+iface.launch()
+
+
+"""
+
 import os, sys
 file_folder = os.path.dirname(os.path.abspath(__file__))
 sys.path.insert(0, os.path.join(file_folder, "utils"))
@@ -16,6 +48,8 @@ iface = gr.Interface(qa_func,
     gr.outputs.Textbox(label="Answer"))
 iface.launch()
 
+"""
+
 """
 import gradio as gr