Upload 2 files

app.py

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+import os
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
+
+
+
+def text(input):
+    from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+    tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-fr")
+    model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-en-fr")
+    input_ids = tokenizer.encode(input, return_tensors="pt")
+    outputs = model.generate(input_ids)
+    decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+    tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-fr-en")
+    model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-fr-en")
+    input_ids = tokenizer.encode(decoded, return_tensors="pt")
+    outputs = model.generate(input_ids)
+    decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return decoded
+
+
+iface = gr.Interface(fn=text,
+                     inputs=[
+                         gr.inputs.Textbox(
+                             lines=2, placeholder=None, label='Sentence'),
+                     ],
+                     outputs=[gr.outputs.JSON(label=None)])
+iface.launch()

requirements.txt

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+gradio
+transformers
+sacremoses
+torch
+sentencepiece