Upload app.py

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import torch
+
+# Load fine-tuned Assamese ↔ English model
+MODEL_NAME = "Sazid2/assamese-english-translator"
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME)
+
+# Translation function
+def translate_text(text):
+    if not text.strip():
+        return "⚠️ Please enter some text."
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+    with torch.no_grad():
+        outputs = model.generate(**inputs, max_length=128)
+    translation = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return translation
+
+# Gradio UI
+demo = gr.Interface(
+    fn=translate_text,
+    inputs=gr.Textbox(label="Enter Assamese or English text", placeholder="Type here..."),
+    outputs=gr.Textbox(label="Translation"),
+    title="🇮🇳 Assamese ↔ English Translator",
+    description="Fine-tuned model for Assamese ↔ English translation using Helsinki-NLP/opus-mt-mul-en base.",
+    theme="soft"
+)
+
+if __name__ == "__main__":
+    demo.launch()