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app.py

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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})
+
+    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("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"])    
+
+

requirements.txt

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+langchain
+Openai
+streamlit
+streamlit_extras
+torch
+accelerate
+transformers