import streamlit as st from typing import Generator from transformers import AutoTokenizer, AutoModelForCausalLM import torch st.set_page_config( page_icon="💬", page_title="Chat App", layout="wide", ) model_name = "JuliaTsk/SuccinctLabs-chat-finetuned" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name, device_map="cpu", low_cpu_mem_usage=True) st.title("ChatGPT-like clone 🎈") def generate_chat_responses(chat_completion) -> Generator[str, None, None]: for chunk in chat_completion: if chunk.choices[0].delta.content: yield chunk.choices[0].delta.content left, right = st.columns([2, 6], vertical_alignment="top") max_tokens_range = 32768 max_tokens = left.slider( label="Max Tokens:", min_value=128, max_value=max_tokens_range, # Default value or max allowed if less value=min(1024, max_tokens_range), step=128, help=f"Adjust the maximum number of tokens (words) for the model's response." ) temperature = left.slider( label="Temperature:", min_value=0.0, max_value=1.0, value=0.7, step=0.01, help=f"Controls randomness: a low value means less random responses." ) if "messages" not in st.session_state: st.session_state.messages = [] for message in st.session_state.messages: avatar = '🤖' if message["role"] == "assistant" else '👨‍💻' with right.chat_message(message["role"], avatar=avatar): right.markdown(message["content"]) prompt = st.chat_input("Say something") if prompt: with right.chat_message("user", avatar='👨‍💻'): right.markdown(prompt) st.session_state.messages.append({"role": "user", "content": prompt}) with right.chat_message("assistant"): inputs = tokenizer(prompt, return_tensors="pt") outputs = model.generate(inputs["input_ids"], max_length=100, num_return_sequences=1) generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True) st.session_state.messages.append({"role": "assistant", "content": generated_text})