Create app.py

app.py

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+import torch
+import gradio as gr
+
+# Load an En-De Transformer model trained on WMT'19 data:
+en2de = torch.hub.load('pytorch/fairseq', 'transformer.wmt19.en-de.single_model', tokenizer='moses', bpe='fastbpe')
+# Load an En-Fr Transformer model trained on WMT'14 data :
+en2fr = torch.hub.load('pytorch/fairseq', 'transformer.wmt14.en-fr', tokenizer='moses', bpe='subword_nmt')
+
+def translate(text, lang):
+  if lang == "French":
+    # Manually tokenize:
+    en_toks = en2fr.tokenize(text)
+
+    # Manually apply BPE:
+    en_bpe = en2fr.apply_bpe(en_toks)
+
+    # Manually binarize:
+    en_bin = en2fr.binarize(en_bpe)
+
+    # Generate five translations with top-k sampling:
+    fr_bin = en2fr.generate(en_bin, beam=5, sampling=True, sampling_topk=20)
+
+    # Convert one of the samples to a string and detokenize
+    fr_sample = fr_bin[0]['tokens']
+    fr_bpe = en2fr.string(fr_sample)
+    fr_toks = en2fr.remove_bpe(fr_bpe)
+    fr = en2fr.detokenize(fr_toks)
+    return fr
+  else: 
+    # Translate from En-De
+    de = en2de.translate(text)
+    return de
+
+inputs = [
+  gr.inputs.Textbox(lines=5, label="Input Text in English"),
+  gr.inputs.Radio(choices=["French", "German"], type="value", label="Output Language")
+]
+  
+outputs = gr.outputs.Textbox(label="Output Text")
+
+title = "Transformer (NMT)"
+description = "Gradio demo for Transformer (NMT). To use it, simply add your text, or click one of the examples to load them. Read more at the links below."
+article = """<p style='text-align: center'><a href='https://arxiv.org/abs/1806.00187'>Scaling Neural Machine Translation</a> | <a href='https://github.com/pytorch/fairseq/'>Github Repo</a></p>"""
+
+examples = [
+  ["Hello world!"],
+  ["PyTorch Hub is a pre-trained model repository designed to facilitate research reproducibility."]
+]
+
+gr.Interface(translate, inputs, outputs, title=title, description=description, article=article, examples=examples, analytics_enabled=False).launch()