try using the chatbot component again

app.py

@@ -1,28 +1,28 @@
 from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
+import gradio as gr
+import gradio as gr
 
-tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
-model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
-
-def predict(input, history=[]):
-    # tokenize the new input sentence
-    new_user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors='pt')
-
-    # append the new user input tokens to the chat history
-    bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1)
-
-    # generate a response 
-    history = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id).tolist()
-
-    # convert the tokens to text, and then split the responses into the right format
-    response = tokenizer.decode(history[0]).split("<|endoftext|>")
-    response = [(response[i], response[i+1]) for i in range(0, len(response)-1, 2)]  # convert to tuples of list
-    return response, history
 
-import gradio as gr
+def chat(message, history):
+    history = history or []
+    if message.startswith("How many"):
+        response = random.randint(1, 10)
+    elif message.startswith("How"):
+        response = random.choice(["Great", "Good", "Okay", "Bad"])
+    elif message.startswith("Where"):
+        response = random.choice(["Here", "There", "Somewhere"])
+    else:
+        response = "I don't know"
+    history.append((message, response))
+    return history, history
 
-gr.Interface(fn=predict,
-             theme="default",
-             css=".footer {display:none !important}",
-             inputs=["text", "state"],
-             outputs=["chatbot", "state"]).launch()
+chatbot = gr.Chatbot(color_map=("green", "gray"))
+demo = gr.Interface(
+    chat,
+    ["text", "state"],
+    [chatbot, "state"],
+    allow_screenshot=False,
+    allow_flagging="never",
+)
+demo.launch()