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app.py

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+import gradio as gr
+
+# Load model from training checkpoint
+from simpletransformers.question_answering import QuestionAnsweringModel
+
+model = QuestionAnsweringModel("bert", "outputs/bert/best_model", use_cuda=False)
+
+# Função para realizar predições
+def perform_prediction(context, question):
+    to_predict = [{"context": context, "qas": [{"question": question, "id": "0"}]}]
+    answers, probabilities = model.predict(to_predict, n_best_size=2)
+    return answers[0]["answer"]
+
+# Criando a interface do Gradio
+iface = gr.Interface(
+    fn=perform_prediction,
+    inputs=[
+        gr.Textbox(placeholder="Insert context here...", label="Contexto"),
+        gr.Textbox(placeholder="Ask a question about the context...", label="Pergunta")
+    ],
+    outputs=gr.Textbox(label="Response"),
+    live=True,
+    theme="compact",
+)
+
+# Iniciando a interface
+iface.launch()

requeriments.txt

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+gradio
+gradio_client
+torch
+simpletransformers