import streamlit as st

from transformers import pipeline

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf")
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf")

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, max_length=200)


# Create a banner using Markdown
st.markdown(
    """
    <div style="min-width: 1000px;">
        <div style="background-color:#f63366;padding:10px;border-radius:10px;">
            <h1 style="color:white;text-align:center;">Red Octopus</h1>
        </div>
        <div style="color:black;text-align:center;">
            <h1>Welcome to the Proposition Management Tool</h1>
        </div>
    </div>
    """,
    unsafe_allow_html=True
)

selectedCity = st.selectbox("Please select the City and the Bank Product for Your Proposition.", ["CharlesTown", "Limburg"])
selectedProduct = st.selectbox("Please select the Product", ["Current", "Mortage", "Credit Card", "Crypto"])
userProposal = st.text_input("Enter your Proposition for Select City and Product")

submit_button = st.button("Submit")

if submit_button:
    st.write("You clicked the Submit button!")
    st.write("Entered text:", userProposal)
    result = pipe(f"<s>[INST] {userProposal} [/INST]")
    st.write(result)