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| import streamlit as st | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| import torch | |
| import random | |
| # Load pre-trained DialoGPT-small model and tokenizer | |
| model_name = "microsoft/DialoGPT-small" | |
| model = AutoModelForCausalLM.from_pretrained(model_name) | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| # Set device to GPU if available for faster inference, otherwise fallback to CPU | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| model.to(device) | |
| # Initialize chat history and conversation context | |
| if 'history' not in st.session_state: | |
| st.session_state['history'] = [] | |
| if 'conversation' not in st.session_state: | |
| st.session_state['conversation'] = [] | |
| # Define multiple system prompts to control bot's behavior | |
| system_prompts = [ | |
| "You are a friendly and professional assistant. You respond in a polite and helpful manner.", | |
| "You are a casual chatbot that likes to engage in fun and interesting conversations, but always stay respectful.", | |
| "You are a helpful assistant. Your goal is to provide clear and precise answers to any questions.", | |
| "You are a compassionate and empathetic listener, always responding with kindness and understanding." | |
| ] | |
| # Select a random system prompt to start the conversation | |
| def get_system_prompt(): | |
| return random.choice(system_prompts) | |
| def generate_response(input_text): | |
| # If it's the first interaction, add the system prompt to the conversation history | |
| if len(st.session_state['history']) == 0: | |
| system_prompt = get_system_prompt() | |
| st.session_state['conversation'].append(f"System: {system_prompt}") | |
| system_input_ids = tokenizer.encode(system_prompt + tokenizer.eos_token, return_tensors='pt').to(device) | |
| st.session_state['history'] = system_input_ids[0].tolist() # Save only the system prompt to history | |
| # Encode the new user input, add end of string token | |
| new_user_input_ids = tokenizer.encode(input_text + tokenizer.eos_token, return_tensors='pt').to(device) | |
| # Append the new user input to the history | |
| if len(st.session_state['history']) > 0: | |
| history_tensor = torch.tensor(st.session_state['history']).unsqueeze(0).to(device) | |
| bot_input_ids = torch.cat([history_tensor, new_user_input_ids], dim=-1) | |
| else: | |
| bot_input_ids = new_user_input_ids | |
| # Generate a response from the model | |
| chat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id, top_k=50, top_p=0.95, temperature=0.7) | |
| # Decode the model's output and add it to the history | |
| chat_history_ids = chat_history_ids[:, bot_input_ids.shape[-1]:] # only take the latest generated tokens | |
| bot_output = tokenizer.decode(chat_history_ids[0], skip_special_tokens=True) | |
| # Update session state history with the new tokens (flattened) | |
| st.session_state['history'] = chat_history_ids[0].tolist() | |
| # Add both user input and bot response to the conversation history for display | |
| st.session_state['conversation'].append(f"You: {input_text}") | |
| st.session_state['conversation'].append(f"Bot: {bot_output}") | |
| return bot_output | |
| # Streamlit Interface | |
| st.title("Chat with DialoGPT") | |
| # Display the conversation history | |
| if st.session_state['conversation']: | |
| for message in st.session_state['conversation']: | |
| st.markdown(f"<p style='color:gray; padding:5px;'>{message}</p>", unsafe_allow_html=True) | |
| # Create input box for user | |
| user_input = st.text_input("You: ", "") | |
| if user_input: | |
| # Generate and display the bot's response | |
| response = generate_response(user_input) | |
| st.write(f"Bot: {response}") | |