Create app.py

app.py

@@ -0,0 +1,16 @@
+import gradio as gr
+from gradio import CSVLogger
+from huggingface_hub import Repository as repo
+import os
+HF_TOKEN = os.getenv('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')#  #'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'
+hf_writer = gr.HuggingFaceDatasetSaver(HF_TOKEN, "tigo_question_answer")
+def question_answer(context, question):
+  predictions = model.predict([{"context": context, "qas": [{"question": question, "id": "0",}],}])
+  prediccion = predictions[0][0]['answer'][0]
+  return prediccion
+iface = gr.Interface(fn=question_answer, inputs=["text", "text"], outputs=["text"],
+                     allow_flagging="manual", #manual
+                     flagging_options=["correcto", "incorrecto"],
+                     flagging_dir='flagged',
+                     flagging_callback=hf_writer)
+iface.launch(enable_queue=True)