Create app.py

app.py

@@ -0,0 +1,57 @@
+from flask import Flask, request, jsonify
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import torch
+import os
+
+app = Flask(__name__)
+
+# Production configuration
+if os.environ.get('FLASK_ENV') == 'production':
+    app.config['DEBUG'] = False
+
+# Load the NLLB-200 model
+model_name = "facebook/nllb-200-distilled-600M"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+
+# Set device
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = model.to(device)
+
+@app.route("/")
+def home():
+    return jsonify({"status": "✅ NLLB Translator is running!"})
+
+@app.route("/translate", methods=["POST"])
+def translate():
+    try:
+        data = request.get_json()
+        text = data.get("text")
+        source_lang = data.get("source_lang")
+        target_lang = data.get("target_lang")
+
+        if not text or not source_lang or not target_lang:
+            return jsonify({
+                "error": "Please provide 'text', 'source_lang', and 'target_lang'."
+            }), 400
+
+        tokenizer.src_lang = source_lang
+        encoded = tokenizer(text, return_tensors="pt").to(device)
+        generated = model.generate(
+            **encoded,
+            forced_bos_token_id=tokenizer.lang_code_to_id[target_lang],
+            max_length=512
+        )
+        translation = tokenizer.batch_decode(generated, skip_special_tokens=True)[0]
+
+        return jsonify({
+            "input": text,
+            "source_lang": source_lang,
+            "target_lang": target_lang,
+            "translation": translation
+        })
+    except Exception as e:
+        return jsonify({"error": str(e)}), 500
+
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=8080)