File size: 3,432 Bytes
c411728 189a7a7 1ee8138 189a7a7 1fea96d 6a1830a 189a7a7 6a1830a 857f215 6a1830a 0b7c3d3 1a97f0c 4608d64 1a97f0c 0b7c3d3 046f5e1 0b7c3d3 77d3d66 bd1a52b 77d3d66 4608d64 eff3674 4608d64 5f5cf3f 0b7c3d3 8f3d647 0b7c3d3 448ad63 4608d64 1a97f0c c50f997 6a1830a 5f5cf3f 589e411 b163ec6 e5daacb 6a1830a 4608d64 1a97f0c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 |
import os
import streamlit as st
import chatbot as demo_chat
from transformers import AutoModelForCausalLM, AutoTokenizer
from langchain.schema import (
HumanMessage,
SystemMessage,
)
from langchain_community.chat_models.huggingface import ChatHuggingFace
from transformers import pipeline
st.title("Hi, I am Chatbot Philio :mermaid:")
st.write("I am your hotel booking assistant for today.")
# tokenizer = AutoTokenizer.from_pretrained("KvrParaskevi/Hotel-Assistant-Attempt4-Llama-2-7b")
tokenizer, model = demo_chat.load_model()
model_identifier = "KvrParaskevi/Hotel-Assistant-Attempt4-Llama-2-7b"
task = "text-generation" # Change this to your model's task
# Load the model using the pipeline
model_pipeline = pipeline(task, model=model,tokenizer=tokenizer)
def render_chat_history(chat_history):
#renders chat history
for message in chat_history:
if(message["role"]!= "system"):
with st.chat_message(message["role"]):
st.markdown(message["content"])
#Application
with st.container():
st.markdown('<div class="scrollable-div">', unsafe_allow_html=True)
#Langchain memory in session cache
if 'memory' not in st.session_state:
st.session_state.memory = demo_chat.demo_miny_memory(model)
#Check if chat history exists in this session
if 'chat_history' not in st.session_state:
st.session_state.chat_history = [
{
"role": "system",
"content": "You are a friendly chatbot who always helps the user book a hotel room based on his/her needs."+
"Before you make a booking you should ask for personal information: first and last name, email and phone number to confirm booking."
+ "Based on the current social norms you wait for the user's response to your proposals.",
},
{"role": "assistant", "content": "Hello, how can I help you today?"},
] #Initialize chat history
if 'model' not in st.session_state:
st.session_state.model = model
render_chat_history(st.session_state.chat_history)
#Set up input text field
#input_text = st.chat_input(placeholder="Here you can chat with our hotel booking model.")
if input_text := st.chat_input(placeholder="Here you can chat with our hotel booking model."):
with st.chat_message("user"):
st.markdown(input_text)
st.session_state.chat_history.append({"role" : "user", "content" : input_text}) #append message to chat history
#chat_response = demo_chat.demo_chain(input_text=input_text, memory=st.session_state.memory, model= chat_model)
#first_answer = chat_response.split("Human")[0] #Because of Predict it prints the whole conversation.Here we seperate the first answer only.
tokenized_chat = tokenizer.apply_chat_template(st.session_state.chat_history, tokenize=True, add_generation_prompt=True, return_tensors="pt")
#st.write(tokenizer.decode(tokenized_chat[0]))
outputs = model.generate(tokenized_chat, max_new_tokens=128)
first_answer = tokenizer.decode(outputs[0][tokenized_chat.shape[1]:],skip_special_tokens=True)
with st.chat_message("assistant"):
st.markdown(first_answer)
st.session_state.chat_history.append({"role": "assistant", "content": first_answer})
st.markdown('</div>', unsafe_allow_html=True)
|