Add application file

app.py

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+import spaces
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_name = 'yuntian-deng/gpt2-small-implicit-cot-multiplication'
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+def preprocess(num):
+    num = num.strip().replace(' ', '')
+    reversed_num = ' '.join(num[::-1])
+    return reversed_num
+
+@spaces.GPU
+def predict_product(num1, num2):
+    input_text = f'{preprocess(num1)} * {preprocess(num2)} ='
+    inputs = tokenizer(input_text, return_tensors='pt').to('cuda' if torch.cuda.is_available() else 'cpu')
+    model.to('cuda' if torch.cuda.is_available() else 'cpu')
+    outputs = model.generate(**inputs, max_new_tokens=40)
+    prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return prediction.strip()
+
+demo = gr.Interface(
+    fn=predict_product,
+    inputs=[gr.Number(label='First Number (up to 9 digits)'), gr.Number(label='Second Number (up to 9 digits)')],
+    outputs='text',
+    title='GPT-2 Multiplication Predictor',
+    description='Enter two numbers up to 9 digits each and get the predicted product.'
+)
+
+demo.launch()

requirements.txt

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+transformers