vangru/Dialogue_System

1import gradio as gr2from transformers import AutoModelForCausalLM, AutoTokenizer3import torch4 5# Load model and tokenizer6model_name = "microsoft/DialoGPT-medium"7 8tokenizer = AutoTokenizer.from_pretrained(model_name)9model = AutoModelForCausalLM.from_pretrained(model_name)10 11# Store conversation history12chat_history_ids = None13 14def respond(message, history):15    global chat_history_ids16 17    # Encode user input18    new_input_ids = tokenizer.encode(message + tokenizer.eos_token, return_tensors='pt')19 20    # Append to chat history21    if chat_history_ids is not None:22        bot_input_ids = torch.cat([chat_history_ids, new_input_ids], dim=-1)23    else:24        bot_input_ids = new_input_ids25 26    # Generate response27    chat_history_ids = model.generate(28        bot_input_ids,29        max_length=1000,30        pad_token_id=tokenizer.eos_token_id,31        do_sample=True,32        top_k=100,33        top_p=0.7,34        temperature=0.835    )36 37    # Decode response38    response = tokenizer.decode(39        chat_history_ids[:, bot_input_ids.shape[-1]:][0],40        skip_special_tokens=True41    )42 43    return response44 45# Create Gradio interface46demo = gr.ChatInterface(47    fn=respond,48    title="Dialogue System using DialoGPT",49    description="A simple conversational AI built with HuggingFace Transformers and Gradio."50)51 52if __name__ == "__main__":53    demo.launch()