Create app.py

app.py

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+import gradio as gr
+from transformers import AutoModelForSeq2SeqLM, NllbTokenizerFast
+import torch
+
+# Load model
+model_id = "ClaudBarbara/Open_Access_Khmer"
+model = AutoModelForSeq2SeqLM.from_pretrained(model_id)
+tokenizer = NllbTokenizerFast.from_pretrained(model_id)
+
+def translate(text, direction):
+    if direction == "English to Khmer":
+        src_lang, tgt_lang = "eng_Latn", "khm_Khmr"
+    else:
+        src_lang, tgt_lang = "khm_Khmr", "eng_Latn"
+    
+    tokenizer.src_lang = src_lang
+    inputs = tokenizer(text, return_tensors="pt", max_length=512, truncation=True)
+    
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            forced_bos_token_id=tokenizer.convert_tokens_to_ids(tgt_lang),
+            max_length=512,
+            num_beams=4
+        )
+    
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+demo = gr.Interface(
+    fn=translate,
+    inputs=[
+        gr.Textbox(label="Input Text", lines=5),
+        gr.Radio(["English to Khmer", "Khmer to English"], label="Direction", value="English to Khmer")
+    ],
+    outputs=gr.Textbox(label="Translation", lines=5),
+    title="Khmer Legal Bridge",
+    description="English-Khmer Legal Translation"
+)
+
+demo.launch()