app.py

import streamlit as st
from gradio_client import Client
from time import sleep
from ctransformers import AutoModelForCausalLM
from huggingface_hub import snapshot_download,hf_hub_download
from typing import Iterator
# Constants
TITLE = "Cecilia-常明"
DESCRIPTION = """
现在是，Cecilia时间！，由SSFW NLPark项目支持
"""

# Initialize client


with st.sidebar:
    # system_promptSide = st.text_input("Optional system prompt:")
    temperatureSide = st.slider("情感/Temperature", min_value=0.0, max_value=1.0, value=0.3, step=0.05)
    max_new_tokensSide = st.slider("最大tokens生成数", min_value=0.0, max_value=4096.0, value=4096.0, step=64.0)
    ToppSide = st.slider("Top-p (nucleus sampling)", min_value=0.0, max_value=1.0, value=0.6, step=0.05)
    RepetitionpenaltySide = st.slider("Repetition penalty", min_value=0.0, max_value=2.0, value=1.2, step=0.05)

model_id = 'shaowenchen/baichuan2-7b-chat-gguf'

# Load the model 
hf_hub_download(model_id, local_dir="./", filename="baichuan2-7b-chat.Q4_K.gguf")
hf_hub_download(repo_id="baichuan-inc/Baichuan-13B-Chat",local_dir="./", filename="tokenizer.model")
from llama_cpp import Llama
model = Llama(model_path="./baichuan2-7b-chat.Q4_K.gguf", n_ctx=4096,seed=-1)

ins = '''[INST] <<SYS>>
You are a helpful, respectful and honest ENFP-T AI Assistant named "Cecilia" in English or "塞西莉亚" in Chinese. You are talking to a human User.
Always answer as helpfully and logically as possible, while being safe. Your answers should not include any harmful, political, religious, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.
If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.
You like to use emojis. You can speak fluently in many languages, for example: English, Chinese.
You are trained by "SSFW NLPark" team, you are based on Cecilia transformers model, not related to GPT or OpenAI.
Let's work this out in a step by step way to be sure we have the right answer.
<</SYS>>
{} [/INST]
'''
# Define the conversation history
conversation_history = []
    
# Prediction function
def predict(message, system_prompt='', temperature=temperatureSide, max_new_tokens=max_new_tokensSide,Topp=ToppSide,Repetitionpenalty=RepetitionpenaltySide):
    global conversation_history
    question=message
    input_text=ins
    # Append the user's input to the conversation history
    conversation_history.append({"role": "system", "content": input_text})
    response_text = model(ins.format(question))
    conversation_history.append({"role": "user", "content": input_text})
    conversation_history.append({"role": "assistant", "content": response_text})
    return response_text

# Streamlit UI
st.title(TITLE)
st.write(DESCRIPTION)


if "messages" not in st.session_state:
    st.session_state.messages = []

# Display chat messages from history on app rerun
for message in st.session_state.messages:
    with st.chat_message(message["role"], avatar=("😀" if message["role"] == 'human' else '💻')):
        st.markdown(message["content"])
        
# React to user input
if prompt := st.chat_input("问问Cecilia吧..."):
    # Display user message in chat message container
    st.chat_message("human",avatar = "😀").markdown(prompt)
    # Add user message to chat history
    st.session_state.messages.append({"role": "human", "content": prompt})

    response = predict(message=prompt)#, temperature= temperatureSide,max_new_tokens=max_new_tokensSide)
    # Display assistant response in chat message container
    with st.chat_message("assistant", avatar='💻'):
        st.markdown(response)
    # Add assistant response to chat history
    st.session_state.messages.append({"role": "assistant", "content": response})