Create app.py

app.py

@@ -0,0 +1,37 @@
+from transformers import pipeline
+import gradio as gr
+
+# 1) Load your fine-tuned QA model from the Hub
+MODEL_ID = "omarbayoumi2/bert-base-qa-squad-colab"
+
+qa = pipeline(
+    "question-answering",
+    model=MODEL_ID,
+    tokenizer=MODEL_ID,
+)
+
+# 2) Inference function
+def answer(question, context):
+    if not question or not context:
+        return "Please provide both a question and a context."
+    result = qa(question=question, context=context)
+    # result is a dict: {'score': ..., 'start': ..., 'end': ..., 'answer': ...}
+    return result["answer"]
+
+# 3) Build Gradio interface
+iface = gr.Interface(
+    fn=answer,
+    inputs=[
+        gr.Textbox(label="Question", placeholder="Ask a question about the context..."),
+        gr.Textbox(label="Context", lines=8, placeholder="Paste the context paragraph here..."),
+    ],
+    outputs=gr.Textbox(label="Answer"),
+    title="BERT-base SQuAD QA Demo",
+    description=(
+        "Fine-tuned `bert-base-uncased` on SQuAD v1.1.\n"
+        "Model: omarbayoumi2/bert-base-qa-squad-colab"
+    ),
+)
+
+if __name__ == "__main__":
+    iface.launch()