Upload app.py

app.py

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+import gradio as gr
+import torch
+from torchtext.data.utils import get_tokenizer
+from torchtext.vocab import build_vocab_from_iterator
+from torchtext.datasets import Multi30k
+from torch import Tensor
+from typing import Iterable, List
+
+from germanToEnglish import Seq2SeqTransformer, translate
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+model = model.load_state_dict(torch.load('./transformer_model.pth', map_location=device))
+model.eval()
+
+if __name__ == "__main__":
+    # Create the Gradio interface
+    iface = gr.Interface(
+        fn=translate,  # Specify the translation function as the main function
+        inputs=[
+            gr.inputs.Textbox(label="Text")
+
+    ],
+    outputs=["text"],  # Define the output type as text
+    cache_examples=False,  # Disable caching of examples
+    title="germanToenglish",  # Set the title of the interface
+    )
+
+    # Launch the interface
+    iface.launch(share=True)