add example

app.py

@@ -4,6 +4,7 @@ model_name = 'armandnlp/gpt2-TOD_finetuned_SGD'
 tokenizer_TOD = AutoTokenizer.from_pretrained(model_name)
 model_TOD = AutoModelForCausalLM.from_pretrained(model_name)
 
+
 def generate_response(prompt):
     input_ids = tokenizer_TOD(prompt, return_tensors="pt").input_ids
     outputs = model_TOD.generate(input_ids, 
@@ -12,10 +13,32 @@ def generate_response(prompt):
                                  eos_token_id=50262)
     return tokenizer_TOD.batch_decode(outputs)[0]
 
+def chat(message, history):
+    history = history or []
+
+    
+    output = generate_response(message)
+    context, response = output.split('<|endofcontext|>')
+
+    history.append((context+'<|endofcontext|>', response))
+    
+    return history
 
 
 import gradio as gr
 
+chatbot = gr.Chatbot(color_map=("gray", "blue"))
+
+iface = gr.Interface(chat,
+                    ["text", "state"],
+                    [chatbot, "state"],
+                    allow_screenshot=False,
+                    allow_flagging="never",
+)
+
+
+
+"""
 iface = gr.Interface(fn=generate_response,
                      inputs="text",
                      outputs="text",
@@ -23,4 +46,5 @@ iface = gr.Interface(fn=generate_response,
                      examples=[["<|context|> <|user|> I'm super hungry ! I want to go to the restaurant.<|endofcontext|>"]],
                      description="Passing in a task-oriented dialogue context generates a belief state, actions to take and a response based on those actions",
                      )
+"""
 iface.launch()