import streamlit as st
from llm import load_llm, response_generator
from sql import csv_to_sqlite, run_sql_query


repo_id = "Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF"
filename = "qwen2.5-coder-1.5b-instruct-q8_0.gguf"
# repo_id = "Qwen/Qwen2.5-0.5B-Instruct-GGUF"
# filename = "qwen2.5-0.5b-instruct-q8_0.gguf"

llm = load_llm(repo_id, filename)

st.title("CSV TO SQL")
st.write("To start, Upload your CSV below 👇")
if st.button("Example prompt"):
    st.session_state.db_name = "sales"
    st.session_state.table_name = "sales"
    csv_to_sqlite("./data/sales.csv", "sales", "sales")

    prompt = "What is the sum, count and average sales?"

    st.session_state.messages.append({"role": "user", "content": prompt})
    response_sql = response_generator(
        db_name=st.session_state.db_name,
        table_name=st.session_state.table_name,
        llm=llm,
        messages=st.session_state.messages,
        question=prompt,
    )
    result = run_sql_query(db_name=st.session_state.db_name, query=response_sql)
    st.session_state.messages.append({"role": "assistant", "content": response_sql})
    st.session_state.messages.append(
        {"role": "assistant", "content": str(result), "result": result}
    )


with st.expander("Upload CSV"):
    csv_file = st.file_uploader(
        "CSV",
    )
    db_name = st.text_input("DB Name")
    table_name = st.text_input("Table Name")
    if st.button("Save"):
        if csv_file and db_name and table_name:
            st.session_state.db_name = db_name
            st.session_state.table_name = table_name

            csv_to_sqlite(csv_file, db_name, table_name)
            st.write("Saved ✅")
        else:
            st.write("Please enter all values")

# Initialize chat history
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"]):
        if "content" in message:
            st.markdown(message["content"])
        if "result" in message:
            st.dataframe(message["result"])

# Accept user input
if prompt := st.chat_input(
    "What is up?",
    disabled=(
        not "db_name" in st.session_state or not "table_name" in st.session_state
    ),
):
    # Add user message to chat history
    st.session_state.messages.append({"role": "user", "content": prompt})
    # Display user message in chat message container
    with st.chat_message("user"):
        st.markdown(prompt)

    # Display assistant response in chat message container
    with st.chat_message("assistant"):
        response_sql = response_generator(
            db_name=st.session_state.db_name,
            table_name=st.session_state.table_name,
            llm=llm,
            messages=st.session_state.messages,
            question=prompt,
        )
        response = st.markdown(response_sql)
        result = run_sql_query(db_name=st.session_state.db_name, query=response_sql)
        st.markdown(result)
        st.table(result)

    # Add assistant response to chat history
    st.session_state.messages.append({"role": "assistant", "content": response_sql})