first work

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
+import torch
+
+# this model was loaded from https://hf.co/models
+model = AutoModelForSeq2SeqLM.from_pretrained("Jayyydyyy/m2m100_418m_tokipona")
+tokenizer = AutoTokenizer.from_pretrained("facebook/m2m100_418M")
+device = 0 if torch.cuda.is_available() else -1
+LANGS = ["English", "toki pona"]
+LANG_CODES = {
+    "English":"en",
+    "toki pona":"tl"
+}
+
+def translate(text, src_lang, tgt_lang):
+    """
+    Translate the text from source lang to target lang
+    """
+
+    src = LANG_CODES.get(src_lang)
+    tgt = LANG_CODES.get(tgt_lang)
+
+    tokenizer.src_lang = src
+    tokenizer.tgt_lang = tgt
+
+    ins = tokenizer(text, return_tensors='pt').to(device)
+
+    gen_args = {
+            'return_dict_in_generate': True,
+            'output_scores': True,
+            'output_hidden_states': True,
+            'length_penalty': 0.0,  # don't encourage longer or shorter output,
+            'num_return_sequences': 3,
+            'num_beams':3,
+            'forced_bos_token_id': tokenizer.lang_code_to_id[tgt]
+        }
+    
+
+    outs = model.generate(**{**ins, **gen_args})
+    output = tokenizer.batch_decode(outs.sequences, skip_special_tokens=True)
+
+    return output
+
+app = gr.Interface(
+    fn=translate,
+    inputs=[
+        gr.components.Textbox(label="Text"),
+        gr.components.Dropdown(label="Source Language", choices=LANGS),
+        gr.components.Dropdown(label="Target Language", choices=LANGS),
+    ],
+    outputs=["text"],
+    examples=[["This is an example!", "English", "toki pona"]],
+    cache_examples=False,
+    title="A simple English / toki pona Neural Translation App",
+    description="A simple English / toki pona Neural Translation App"
+)
+
+app.launch()

requirements.txt

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+torch
+transformers
+gradio