Update app.py

app.py

@@ -1,24 +1,33 @@
 import gradio as gr
 from transformers import AutoTokenizer
+import spaces
 
 # Function to load and tokenize the message using the selected tokenizer
-def tokenize(message, history, tokenizer_name):
+@spaces.GPU
+def tokenize(message, tokenizer_name):
     try:
         tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
         return str(tokenizer.encode(message))
     except Exception as e:
         return f"Error loading tokenizer: {str(e)}"
 
+# Wrapper function to handle chat interaction
+def chat(message, history, tokenizer_name):
+    tokenized_message = tokenize(message, tokenizer_name)
+    history.append((message, tokenized_message))
+    return history, history
+
 # Gradio interface
-def create_chat_interface():
-    with gr.Blocks() as demo:
-        tokenizer_input = gr.Textbox(placeholder="Enter the tokenizer name (e.g., gpt2, bert-base-uncased)", label="Tokenizer Name")
-        chatbot = gr.ChatInterface(fn=tokenize, inputs=["text", "state", tokenizer_input])
-        demo.append(tokenizer_input)
-        demo.append(chatbot)
-    return demo
+with gr.Blocks() as demo:
+    tokenizer_input = gr.Textbox(placeholder="Enter the tokenizer name (e.g., gpt2, bert-base-uncased)", label="Tokenizer Name")
+    chatbot = gr.Chatbot()
+    msg = gr.Textbox()
+    clear = gr.Button("Clear")
+
+    def clear_history():
+        return [], []
+
+    msg.submit(chat, inputs=[msg, chatbot, tokenizer_input], outputs=[chatbot, chatbot])
+    clear.click(clear_history, outputs=[chatbot, chatbot])
 
-# Launch the interface
-if __name__ == "__main__":
-    demo = create_chat_interface()
-    demo.launch()
+demo.launch()