init

app.py

@@ -0,0 +1,96 @@
+import os
+import torch
+import gradio as gr
+import time
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
+from flores200_codes import flores_codes
+
+
+def load_models():
+    # build model and tokenizer
+    model_name_dict = {
+        "nllb-distilled-600M": "facebook/nllb-200-distilled-600M",
+        #'nllb-1.3B': 'facebook/nllb-200-1.3B',
+        #'nllb-distilled-1.3B': 'facebook/nllb-200-distilled-1.3B',
+        #'nllb-3.3B': 'facebook/nllb-200-3.3B',
+    }
+
+    model_dict = {}
+
+    for call_name, real_name in model_name_dict.items():
+        print("\tLoading model: %s" % call_name)
+        model = AutoModelForSeq2SeqLM.from_pretrained(real_name)
+        tokenizer = AutoTokenizer.from_pretrained(real_name)
+        model_dict[call_name + "_model"] = model
+        model_dict[call_name + "_tokenizer"] = tokenizer
+
+    return model_dict
+
+
+def translation(source, target, text):
+    if len(model_dict) == 2:
+        model_name = "nllb-distilled-600M"
+
+    start_time = time.time()
+    source = flores_codes[source]
+    target = flores_codes[target]
+
+    model = model_dict[model_name + "_model"]
+    tokenizer = model_dict[model_name + "_tokenizer"]
+
+    translator = pipeline(
+        "translation",
+        model=model,
+        tokenizer=tokenizer,
+        src_lang=source,
+        tgt_lang=target,
+    )
+    output = translator(text, max_length=400)
+
+    end_time = time.time()
+
+    output = output[0]["translation_text"]
+    result = {
+        "inference_time": end_time - start_time,
+        "source": source,
+        "target": target,
+        "result": output,
+    }
+    return result
+
+
+if __name__ == "__main__":
+    print("\tinit models")
+
+    global model_dict
+
+    model_dict = load_models()
+
+    # define gradio demo
+    lang_codes = list(flores_codes.keys())
+    # inputs = [gr.inputs.Radio(['nllb-distilled-600M', 'nllb-1.3B', 'nllb-distilled-1.3B'], label='NLLB Model'),
+    inputs = [
+        gr.inputs.Dropdown(lang_codes, default="English", label="Source"),
+        gr.inputs.Dropdown(lang_codes, default="Korean", label="Target"),
+        gr.inputs.Textbox(lines=5, label="Input text"),
+    ]
+
+    outputs = gr.outputs.JSON()
+
+    title = "NLLB distilled 600M demo"
+
+    demo_status = "Demo is running on CPU"
+    description = (
+        f"Details: https://github.com/facebookresearch/fairseq/tree/nllb. {demo_status}"
+    )
+    examples = [["English", "Korean", "Hi. nice to meet you"]]
+
+    gr.Interface(
+        translation,
+        inputs,
+        outputs,
+        title=title,
+        description=description,
+        examples=examples,
+        examples_per_page=50,
+    ).launch()