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| import streamlit as st | |
| from transformers import T5ForConditionalGeneration, T5Tokenizer | |
| import torch | |
| import random | |
| # Load pre-trained T5 model and tokenizer | |
| model_name = "t5-small" # You can use "t5-base" or "t5-large" for better quality but slower response | |
| model = T5ForConditionalGeneration.from_pretrained(model_name) | |
| tokenizer = T5Tokenizer.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 helpful assistant. Respond in a polite, friendly, and informative manner.", | |
| "You are a conversational chatbot. Provide friendly, engaging, and empathetic responses.", | |
| "You are an informative assistant. Respond clearly and concisely to any questions asked.", | |
| "You are a fun, casual chatbot. Keep the conversation light-hearted and interesting." | |
| ] | |
| # 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 = f"conversation: {system_prompt} " | |
| st.session_state['history'].append(system_input) | |
| # Prepare the user input by appending it to the history | |
| user_input = f"conversation: {input_text} " | |
| # Concatenate history (system prompt + user input) | |
| full_input = "".join(st.session_state['history']) + user_input | |
| # Tokenize input text and generate response from the model | |
| input_ids = tokenizer.encode(full_input, return_tensors="pt").to(device) | |
| outputs = model.generate(input_ids, max_length=1000, num_beams=5, top_p=0.95, temperature=0.7, pad_token_id=tokenizer.eos_token_id) | |
| # Decode the model's output | |
| bot_output = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| # Update the history with the new user input and the model's output | |
| st.session_state['history'].append(user_input) | |
| st.session_state['history'].append(f"bot: {bot_output} ") | |
| # 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 T5") | |
| # 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}") | |