Create app.py

app.py

@@ -0,0 +1,51 @@
+import torch
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+import gradio as gr
+
+# Load model và tokenizer
+model_name = "b3x0m/hirashiba-xomdich-tokenizer"
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name).to(device)
+
+def translate_text(input_text):
+    lines = input_text.split('\n')  # Tách từng dòng
+    translated_lines = []
+    
+    for line in lines:
+        raw_text = line.strip()
+        if not raw_text:
+            translated_lines.append('')  # Giữ dòng trống
+            continue
+        
+        # Tokenize input
+        inputs = tokenizer(raw_text, return_tensors="pt", padding=True, truncation=True).to(device)
+        
+        # Dịch với mô hình (không cần tính gradient)
+        with torch.no_grad():
+            output_tokens = model.generate(**inputs, max_length=512)
+        
+        # Giải mã kết quả
+        translated_text = tokenizer.decode(output_tokens[0], skip_special_tokens=True)
+        translated_lines.append(translated_text)
+    
+    return '\n'.join(translated_lines)
+
+if __name__ == '__main__':
+    with gr.Blocks() as app:
+        gr.Markdown('## Chinese to Vietnamese Translation')
+        
+        with gr.Row():
+            with gr.Column(scale=1):
+                input_text = gr.Textbox(label='Input Chinese Text', lines=5, placeholder='Enter Chinese text here...')
+                translate_button = gr.Button('Translate')
+                output_text = gr.Textbox(label='Output Vietnamese Text', lines=5, interactive=False)
+        
+        translate_button.click(
+            fn=translate_text,
+            inputs=input_text,
+            outputs=output_text
+        )
+
+    app.launch()