Create app.py

app.py

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+from transformers import AutoModelForQuestionAnswering, AutoTokenizer
+import torch
+import gradio as gr
+
+
+model = AutoModelForQuestionAnswering.from_pretrained("msures3/distilbert-base-squad")
+tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
+
+def generate_response(context, question):
+    inputs = tokenizer(question, context, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**inputs)
+        answer_start_index = outputs.start_logits.argmax()
+        answer_end_index = outputs.end_logits.argmax()
+        predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1]
+        predicted_answers = tokenizer.decode(predict_answer_tokens)
+    return predicted_answers
+
+
+inputs = [
+    gr.Textbox(label="Enter Context"),
+    gr.Textbox(label="Enter Question")
+]
+outputs = gr.Textbox(label="Predicted Answer")
+
+
+app = gr.Interface(
+    fn=generate_response,
+    inputs=inputs,
+    outputs=outputs,
+    title="Context Based Question Answering",
+    description="Enter a context and a question to get the predicted answer.",
+    examples=[
+        ["This is a sample context. The quick brown fox jumps over the lazy dog.", "What animal jumps over the dog?"],
+        ["The capital of France is Paris. It is a beautiful city with many attractions.", "What is the capital of France?"]
+    ]
+)
+
+app.launch()