Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# モデルID
+model_id = "tencent/HY-MT1.5-1.8B"
+
+# 環境に合わせてデバイスと精度を自動選択
+# Freeスペース(CPU)の場合はfloat32、GPUがある場合はfloat16を使用
+if torch.cuda.is_available():
+    device = "cuda"
+    dtype = torch.float16
+else:
+    device = "cpu"
+    dtype = torch.float32
+
+print(f"Loading model on {device} with {dtype}...")
+
+# トークナイザーとモデルの読み込み
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    device_map=device, # autoではなく明示的に指定
+    torch_dtype=dtype
+)
+
+def translate_text(source_text, target_lang):
+    # プロンプトの切り替えロジック
+    if "Chinese" in target_lang or "中文" in target_lang:
+        prompt = f"将以下文本翻译为{target_lang}，注意只需要输出翻译后的结果，不要额外解释：\n{source_text}"
+    else:
+        prompt = f"Translate the following segment into {target_lang}, without additional explanation.\n{source_text}"
+
+    messages = [{"role": "user", "content": prompt}]
+    
+    # 入力処理
+    text_input = tokenizer.apply_chat_template(
+        messages,
+        tokenize=True,
+        add_generation_prompt=False, 
+        return_tensors="pt"
+    ).to(device)
+
+    # 生成実行
+    with torch.no_grad():
+        generated_ids = model.generate(
+            text_input,
+            max_new_tokens=1024,
+            temperature=0.7,
+            top_p=0.6,
+            repetition_penalty=1.05
+        )
+
+    # 出力処理
+    input_length = text_input.shape[1]
+    response = generated_ids[0][input_length:]
+    decoded_output = tokenizer.decode(response, skip_special_tokens=True)
+    
+    return decoded_output
+
+# UIの構築
+langs = ["Japanese", "English", "Chinese", "Korean", "French", "German", "Spanish"]
+
+with gr.Blocks() as demo:
+    gr.Markdown("# 🚀 HY-MT1.5-1.8B Translator (Spaces)")
+    gr.Markdown("Tencentの1.8Bモデルを使用した翻訳デモです。")
+    
+    with gr.Row():
+        with gr.Column():
+            input_text = gr.Textbox(label="原文 (Source Text)", lines=5, placeholder="ここに入力...")
+            target_lang = gr.Dropdown(choices=langs, value="English", label="翻訳先 (Target Language)")
+            submit_btn = gr.Button("翻訳 (Translate)", variant="primary")
+        
+        with gr.Column():
+            output_text = gr.Textbox(label="結果 (Result)", lines=5, interactive=False)
+
+    submit_btn.click(
+        fn=translate_text,
+        inputs=[input_text, target_lang],
+        outputs=output_text
+    )
+
+# 起動
+demo.launch()