Create app.py

app.py

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+import gradio as gr
+from transformers import AutoModelForQuestionAnswering, AutoTokenizer
+
+# Load the pretrained model and tokenizer
+model_name = "JPeace18/vit-base-patch16-224-in21k-finetuned-lora-food101"  # Replace with your desired model
+model = AutoModelForQuestionAnswering.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Define the function to generate the model's answer
+def get_answer(question):
+    inputs = tokenizer(question, return_tensors="pt")
+    outputs = model(**inputs)
+    answer_start = torch.argmax(outputs.start_logits)
+    answer_end = torch.argmax(outputs.end_logits) + 1
+    answer = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(inputs["input_ids"][0][answer_start:answer_end]))
+    return answer
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=get_answer,
+    inputs="text",
+    outputs="text",
+    live=True,
+    title="Question Answering Model",
+    description="Ask a question, and the model will provide an answer.",
+)
+
+# Launch the Gradio interface
+iface.launch()