Update app.py

app.py

@@ -1,39 +1,7 @@
-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")
@@ -47,4 +15,4 @@ examples = [
   ["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()
+gr.Interface.load("huggingface/facebook/wmt19-en-de", inputs, outputs, title=title, description=description, article=article, examples=examples, analytics_enabled=False).launch()