Create app.py

app.py

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+import torch
+from transformers import AutoTokenizer, AutoModelForQuestionAnswering
+import gradio as gr
+
+model_name = "checkpoint-1700"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForQuestionAnswering.from_pretrained(model_name)
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model.to(device)
+
+def answer_question(question, context):
+    if not question.strip() or not context.strip():
+        return "Soru ve metin boş olamaz!"
+
+    inputs = tokenizer(question, context, return_tensors="pt", truncation=True)
+    inputs = {k: v.to(device) for k, v in inputs.items()}
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+
+    answer_start = torch.argmax(outputs.start_logits)
+    answer_end = torch.argmax(outputs.end_logits) + 1
+
+    input_ids = inputs["input_ids"][0]
+    answer = tokenizer.convert_tokens_to_string(
+        tokenizer.convert_ids_to_tokens(input_ids[answer_start:answer_end].cpu())
+    )
+
+    return answer.strip()
+
+demo = gr.Interface(
+    fn=answer_question,
+    inputs=[
+        gr.Textbox(label="Soru", placeholder="Örn: Türkiye'nin başkenti neresidir ?"),
+        gr.Textbox(
+            label="Metin", 
+            placeholder="Metni buraya girin...",
+            lines=10
+        )
+    ],
+    outputs=gr.Textbox(label="Cevap"),
+    title="BERT Soru-Cevap Sistemi",
+    description="Metin ve sorunuzu girin ve BERT modeli cevabı metin içerisinden çıkarsın.",
+    theme="default",
+)
+
+if __name__ == "__main__":
+    demo.launch()