Update app.py

app.py

@@ -11,10 +11,25 @@ base = AutoModelForCausalLM.from_pretrained(base_model)
 model = PeftModel.from_pretrained(base, adapter_model)
 model.eval()
 
-def infer(text):
-    inputs = tokenizer(text, return_tensors="pt").to(model.device)
-    outputs = model.generate(**inputs, max_new_tokens=50)
-    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+def translate(model, tokenizer, input_text, direction=0, max_new_tokens=256):
+    if direction == 0:
+        prompt = f"Translate Bangla to Sylheti: {input_text}\nOutput:"
+    else:
+        prompt = f"Translate Sylheti to Bangla: {input_text}\nOutput:"
+
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    output_ids = model.generate(**inputs, max_new_tokens=max_new_tokens)
+    return tokenizer.decode(output_ids[0], skip_special_tokens=True)
+
+# Gradio function wrapper
+def infer(text, direction):
+    return translate(model, tokenizer, text, direction=direction)
+
+demo = gr.Interface(
+    fn=infer,
+    inputs=[gr.Textbox(label="Input Text"), gr.Radio(["Bangla to Sylheti", "Sylheti to Bangla"], type="index", label="Translation Direction")],
+    outputs="text",
+    title="Phi-2 Sylheti Translator"
+)
 
-demo = gr.Interface(fn=infer, inputs="text", outputs="text", title="Phi-2 Sylheti Translator")
 demo.launch()