Create app.py

app.py

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+import torch
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import gradio as gr
+
+# Load model
+model_path = "./banglat5_bn_sy"  # path inside Space
+tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_path)
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+# Translation function
+def translate(text, source_lang):
+    if source_lang == "Bangla":
+        prefix = "<BN>"
+    elif source_lang == "Sylheti":
+        prefix = "<SY>"
+    else:
+        return "Invalid language selected."
+
+    input_text = f"{prefix} {text}"
+    inputs = tokenizer(input_text, return_tensors="pt", truncation=True, padding=True).to(device)
+
+    with torch.no_grad():
+        outputs = model.generate(
+            input_ids=inputs["input_ids"],
+            attention_mask=inputs["attention_mask"],
+            max_length=128,
+            num_beams=4,
+            early_stopping=True
+        )
+    
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# Gradio interface
+iface = gr.Interface(
+    fn=translate,
+    inputs=[
+        gr.Textbox(label="Input Text"),
+        gr.Radio(["Bangla", "Sylheti"], label="Source Language")
+    ],
+    outputs=gr.Textbox(label="Translated Text"),
+    title="Bangla ↔ Sylheti Dialect Translator (Fine-tuned T5)",
+    description="Translate between Bangla and Sylheti using a LoRA-finetuned Flan-T5 model."
+)
+
+iface.launch()