Create app.py

app.py

@@ -0,0 +1,47 @@
+from flask import Flask, render_template, request, jsonify
+from transformers import AutoModelForSeq2SeqLM, NllbTokenizerFast
+import torch
+
+app = Flask(__name__)
+
+print("Loading model...")
+MODEL_ID = "ClaudBarbara/Open_Access_Khmer"
+model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_ID)
+tokenizer = NllbTokenizerFast.from_pretrained(MODEL_ID)
+print("Model loaded!")
+
+def translate(text, src_lang, tgt_lang):
+    if not text.strip():
+        return ""
+    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)
+
+@app.route("/")
+def index():
+    return render_template("index.html")
+
+@app.route("/translate", methods=["POST"])
+def translate_endpoint():
+    data = request.json
+    text = data.get("text", "")
+    direction = data.get("direction", "en-km")
+    if direction == "en-km":
+        src_lang, tgt_lang = "eng_Latn", "khm_Khmr"
+    else:
+        src_lang, tgt_lang = "khm_Khmr", "eng_Latn"
+    try:
+        result = translate(text, src_lang, tgt_lang)
+        return jsonify({"success": True, "translation": result})
+    except Exception as e:
+        return jsonify({"success": False, "error": str(e)})
+
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=7860)