Update app.py

app.py

@@ -5,44 +5,32 @@ import gradio as gr
 tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-large")
 model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-large")
 
-# Initialize chat history
-chat_history_ids = None
-
-# Define a function to process user input and generate bot response
-def chat_cpu(user_input, chat_history=None):
-    global chat_history_ids
-
-    # If chat history is provided, use it
-    if chat_history is not None:
-        chat_history_ids = chat_history
+def chat_with_history(message, chat_history=None):
+    # Initialize chat history if not provided
+    if chat_history is None:
+        chat_history = []
 
     # Encode the new user input, add the eos_token, and return a tensor in PyTorch
-    new_user_input_ids = tokenizer.encode(user_input + tokenizer.eos_token, return_tensors='pt')
+    new_user_input_ids = tokenizer.encode(message + tokenizer.eos_token, return_tensors='pt')
 
     # Append the new user input tokens to the chat history
-    bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if chat_history_ids is not None else new_user_input_ids
+    bot_input_ids = torch.cat([tokenizer.encode(pair[0] + tokenizer.eos_token, return_tensors='pt') for pair in chat_history] + [new_user_input_ids], dim=-1)
 
     # Generate a response while limiting the total chat history to 1000 tokens
     chat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)
 
-    # Pretty print last output tokens from bot
+    # Decode the last output tokens from bot
     response = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
 
-    # Return the response and updated chat history
-    return response, chat_history_ids
-
-# Define a Gradio interface
-iface = gr.Interface(
-    fn=chat_cpu,
-    inputs=["text", gr.Textbox(placeholder="Chat history will appear here", lines=10)],
-    outputs=[gr.TextElement(text="BOT", id="bot_history", default="Chat AI"),
-             gr.TextElement(text="USER", id="user_history", default="")],
-    live=True,
-    layout="vertical",
-    theme="compact",
-    title="Chat AI",
-    css=".output {flex-direction: column-reverse;}",
-)
-
-# Launch the Gradio interface
-iface.launch()
+    # Update the chat history with the new user message and bot response
+    chat_history.append([message, response])
+
+    return response, chat_history
+
+demo = gr.ChatInterface(
+        fn=chat_with_history,
+        examples=["hey how are you ?", "hola", "Yo!"],
+        title="Multi Chat Bot"
+    )
+
+demo.launch()