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Update app.py
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app.py
CHANGED
@@ -5,44 +5,32 @@ import gradio as gr
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tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-large")
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model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-large")
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def chat_cpu(user_input, chat_history=None):
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global chat_history_ids
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# If chat history is provided, use it
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if chat_history is not None:
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chat_history_ids = chat_history
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# Encode the new user input, add the eos_token, and return a tensor in PyTorch
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new_user_input_ids = tokenizer.encode(
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# Append the new user input tokens to the chat history
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bot_input_ids = torch.cat([
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# Generate a response while limiting the total chat history to 1000 tokens
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chat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)
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#
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response = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
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#
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title="Chat AI",
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css=".output {flex-direction: column-reverse;}",
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)
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# Launch the Gradio interface
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iface.launch()
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tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-large")
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model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-large")
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def chat_with_history(message, chat_history=None):
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# Initialize chat history if not provided
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if chat_history is None:
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chat_history = []
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# Encode the new user input, add the eos_token, and return a tensor in PyTorch
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new_user_input_ids = tokenizer.encode(message + tokenizer.eos_token, return_tensors='pt')
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# Append the new user input tokens to the chat history
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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)
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# Generate a response while limiting the total chat history to 1000 tokens
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chat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)
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# Decode the last output tokens from bot
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response = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
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# Update the chat history with the new user message and bot response
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chat_history.append([message, response])
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return response, chat_history
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demo = gr.ChatInterface(
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fn=chat_with_history,
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examples=["hey how are you ?", "hola", "Yo!"],
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title="Multi Chat Bot"
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)
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demo.launch()
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