Create app.py

app.py

@@ -0,0 +1,41 @@
+from transformers import DistilBertForQuestionAnswering, DistilBertConfig, DistilBertTokenizerFast
+import torch
+
+model = DistilBertForQuestionAnswering(DistilBertConfig.from_pretrained('distilbert/distilbert-base-multilingual-cased')).to("cpu")
+st_dict = torch.load("save/best_f1/checkpoint/QazDistilBERT.pt")
+model.load_state_dict(st_dict)
+tokenizer = DistilBertTokenizerFast.from_pretrained("dappyx/QazDistilbertFast-tokenizerV3")
+
+import gradio as gr
+
+def qa_pipeline(text,question):
+  inputs = tokenizer(question, text, return_tensors="pt")
+  input_ids = inputs['input_ids'].to("cpu")
+  attention_mask = inputs['attention_mask'].to("cpu")
+  outputs = model(input_ids=input_ids,attention_mask=attention_mask)
+
+  start_index = torch.argmax(outputs.start_logits, dim=-1).item()
+  end_index = torch.argmax(outputs.end_logits, dim=-1).item()
+
+  predict_answer_tokens = inputs.input_ids[0, start_index : end_index + 1]
+  return tokenizer.decode(predict_answer_tokens)
+
+def answer_question(context, question):
+    result = qa_pipeline(context, question)
+    return result
+
+
+# Создаем интерфейс
+iface = gr.Interface(
+    fn=answer_question, 
+    inputs=[
+        gr.Textbox(lines=10, label="Context"),
+        gr.Textbox(lines=2, label="Question")
+    ], 
+    outputs="text",
+    title="Question Answering Model",
+    description="Введите контекст и задайте вопрос, чтобы получить ответ."
+)
+
+# Запускаем интерфейс
+iface.launch()