Update app.py

app.py

@@ -2,27 +2,33 @@ import gradio as gr
 from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
 
-# Load Sinhala LLM
+# Use LLaMA architecture explicitly
 model_name = "polyglots/SinLlama_v01"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
 model = AutoModelForCausalLM.from_pretrained(
     model_name,
     torch_dtype=torch.float16,
     device_map="auto"
 )
 
-def chat_with_sinllama(user_input, history=[]):
-    inputs = tokenizer(user_input, return_tensors="pt").to(model.device)
-    outputs = model.generate(**inputs, max_new_tokens=200)
+def chat_with_sinllama(message, history=[]):
+    inputs = tokenizer(message, return_tensors="pt").to(model.device)
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=200,
+        temperature=0.7,
+        do_sample=True
+    )
     reply = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    history.append((user_input, reply))
+    history.append((message, reply))
     return history, history
 
-# Gradio Chatbot UI
+# Gradio app
 with gr.Blocks() as demo:
-    chatbot = gr.Chatbot()
-    msg = gr.Textbox(label="Type your question in Sinhala")
-    clear = gr.Button("Clear Chat")
+    chatbot = gr.Chatbot(label="SinLlama (Sinhala Chatbot)")
+    msg = gr.Textbox(label="Type your message in Sinhala")
+    clear = gr.Button("Clear")
 
     msg.submit(chat_with_sinllama, [msg, chatbot], [chatbot, chatbot])
     clear.click(lambda: None, None, chatbot, queue=False)