Upload translator_app.py

translator_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
+
+# Define your model, tokenizer, and other necessary components here
+# Ensure you have imported all necessary libraries
+
+# Load your transformer model
+model = Seq2SeqTransformer(NUM_ENCODER_LAYERS, NUM_DECODER_LAYERS, EMB_SIZE,
+                           NHEAD, SRC_VOCAB_SIZE, TGT_VOCAB_SIZE, FFN_HID_DIM)
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+model.load_state_dict(torch.load('./transformer_model.pth', map_location=device))
+model.eval()
+
+def translate(model: torch.nn.Module, src_sentence: str):
+    model.eval()
+    src = text_transform[SRC_LANGUAGE](src_sentence).view(-1, 1)
+    num_tokens = src.shape[0]
+    src_mask = (torch.zeros(num_tokens, num_tokens)).type(torch.bool)
+    tgt_tokens = greedy_decode(
+        model,  src, src_mask, max_len=num_tokens + 5, start_symbol=BOS_IDX).flatten()
+    return " ".join(vocab_transform[TGT_LANGUAGE].lookup_tokens(list(tgt_tokens.cpu().numpy()))).replace("<bos>", "").replace("<eos>", "")
+
+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)