import streamlit as st
import pandas as pd
import sqlite3
from llama_index.core import StorageContext, load_index_from_storage
from llama_index.llms.ollama import Ollama
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.core import PromptTemplate
import os


version = 2.3

# Initialize the SQLite3 database
conn = sqlite3.connect('qa.db')
c = conn.cursor()
# Update the table creation to include the version column
c.execute('CREATE TABLE IF NOT EXISTS qa (question TEXT, answer TEXT, version REAL)')
conn.commit()

# Read the LLM Model Description from a file
def read_description_from_file(file_path):
    with open(file_path, 'r') as file:
        return file.read()

# Define the folder containing the saved index
INDEX_OUTPUT_PATH = "./output_index"

# Ensure the output directory exists
if not os.path.exists(INDEX_OUTPUT_PATH):
    raise ValueError(f"Index directory {INDEX_OUTPUT_PATH} does not exist")

# Setup LLM and embedding model
llm = Ollama(model="llama3", request_timeout=120.0)
embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-large-en-v1.5", trust_remote_code=True)

# To load the index later, set up the storage context
storage_context = StorageContext.from_defaults(persist_dir=INDEX_OUTPUT_PATH)
loaded_index = load_index_from_storage(embed_model=embed_model, storage_context=storage_context)

# Define a query engine (assuming it needs the LLM and embedding model)
query_engine = loaded_index.as_query_engine(llm=llm, embed_model=embed_model)

# Customise prompt template
# Read the prompt template from a file
qa_prompt_tmpl_str = read_description_from_file("tab2_pe.txt")
qa_prompt_tmpl = PromptTemplate(qa_prompt_tmpl_str)

query_engine.update_prompts(
    {"response_synthesizer:text_qa_template": qa_prompt_tmpl}
)

# Save the question and answer to the SQLite3 database
def save_to_db(question, answer, version):
    c.execute('INSERT INTO qa (question, answer, version) VALUES (?, ?, ?)', (question, answer, version))
    conn.commit()

# Fetch all data from the SQLite3 database
def fetch_from_db():
    c.execute('SELECT * FROM qa')
    return c.fetchall()

def main():
    st.title("How Much AI Can Assist Our Email Replying System Of Our Council?")

    tab1, tab2, tab3 = st.tabs(["LLM Model Description", "Ask a Question", "View Q&A History"])

    with tab1:
        st.subheader("LLM Model Description")
        description = read_description_from_file("tab1_intro.txt")
        st.write(description)

    with tab2:
        st.subheader("Ask a Question (Please try to focus on council tax)")
        question = st.text_input("Enter your question:")
        if st.button("Get Answer"):
            if question:
                try:
                    response = query_engine.query(question)

                    # Try to extract the generated text
                    try:
                        # Extract the text from the response object (assuming it has a `text` attribute or method)
                        if hasattr(response, 'text'):
                            answer = response.text
                        else:
                            answer = str(response)

                    except AttributeError as e:
                        st.error(f"Error extracting text from response: {e}")
                        answer = "Sorry, could not generate an answer."

                    st.write(f"**Answer:** {answer}")

                    # Save question and answer to database
                    save_to_db(question, answer, version)
                except Exception as e:
                    st.error(f"An error occurred: {e}")
            else:
                st.warning("Please enter a question")

    with tab3:
        st.subheader("View Q&A History")
        qa_data = fetch_from_db()
        if qa_data:
            df = pd.DataFrame(qa_data, columns=["Question", "Answer", "Version"])
            st.dataframe(df)
        else:
            st.write("No data available")

if __name__ == "__main__":
    main()