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| import os | |
| import streamlit as st | |
| from streamlit_extras.let_it_rain import rain | |
| from transformers import AutoTokenizer | |
| from langchain.chat_models import ChatOpenAI | |
| from langchain.schema import AIMessage,HumanMessage,SystemMessage | |
| from langchain import PromptTemplate, LLMChain | |
| from langchain import HuggingFacePipeline | |
| import transformers | |
| import torch | |
| from huggingface_hub import login | |
| def get_response(question): | |
| st.session_state.sessionMessages.append(HumanMessage(content=question)) | |
| assistant_answer = chat(st.session_state.sessionMessages ) | |
| st.session_state.sessionMessages.append(AIMessage(content=assistant_answer.content)) | |
| return assistant_answer.content | |
| def get_sentiment(user_input, llm_chain): | |
| result = llm_chain.run(user_input) | |
| return result.lower() | |
| def init_llama_model(): | |
| model = "meta-llama/Llama-2-7b-chat-hf" | |
| tokenizer = AutoTokenizer.from_pretrained(model) | |
| pipeline = transformers.pipeline( | |
| "text-generation", | |
| model=model, | |
| tokenizer=tokenizer, | |
| torch_dtype=torch.bfloat16, | |
| device_map="auto", | |
| max_length=200, | |
| do_sample=True, | |
| top_k=10, | |
| num_return_sequences=1, | |
| eos_token_id=tokenizer.eos_token_id | |
| ) | |
| llm = HuggingFacePipeline(pipeline = pipeline, model_kwargs = {'temperature':0.3}) | |
| template = '''Classify the text into neutral, negative, or positive. Reply with only one word: Positive, Negative, or Neutral. | |
| Examples: | |
| Text: You will simply love the Big variety of snacks (sweet and savoury) and you can't get wrong if you choose the place for a quick meal or coffee. | |
| Sentiment: Positive. | |
| Text: I got food poisoning | |
| Sentiment: Negative. | |
| Text: {text} | |
| Sentiment: | |
| ''' | |
| prompt = PromptTemplate(template=template, input_variables=["text"]) | |
| llm_chain = LLMChain(prompt=prompt, llm=llm) | |
| return llm_chain | |
| chat = ChatOpenAI(model="gpt-3.5-turbo", temperature=0) | |
| llm_chain = init_llama_model() | |
| st.set_page_config(page_title="HomeX Assistant", page_icon=":robot:") | |
| st.header("Hey, I'm your HomeX Assistant") | |
| if "sessionMessages" not in st.session_state: | |
| st.session_state.sessionMessages = [SystemMessage(content="You are a helpful assistant.")] | |
| if "messages" not in st.session_state: | |
| st.session_state.messages = [] | |
| if user_input := st.chat_input("Welcome Home,Say something"): | |
| assistant_input = get_response(user_input) | |
| # add user input to history | |
| st.session_state.messages.append({"role": "user", "content": user_input}) | |
| # add assistant input to history | |
| st.session_state.messages.append({"role": "assistant", "content": assistant_input}) | |
| # sentiment analysis | |
| sentiment = get_sentiment(user_input, llm_chain) | |
| if sentiment == "negative": | |
| rain( | |
| emoji="😭", | |
| font_size=30, # the size of emoji | |
| falling_speed=3, # speed of raining | |
| animation_length="infinite", # for how much time the animation will happen | |
| ) | |
| elif sentiment == "neutral": | |
| rain( | |
| emoji="😐", | |
| font_size=30, # the size of emoji | |
| falling_speed=3, # speed of raining | |
| animation_length="infinite", # for how much time the animation will happen | |
| ) | |
| elif sentiment == "positive": | |
| rain( | |
| emoji="🤩", | |
| font_size=30, # the size of emoji | |
| falling_speed=3, # speed of raining | |
| animation_length="infinite", # for how much time the animation will happen | |
| ) | |
| # display chat history | |
| for message in st.session_state.messages: | |
| with st.chat_message(message["role"]): | |
| st.markdown(message["content"]) | |