import streamlit as st
import pandas as pd

from apis import ask, run_sql
from utils import format_sql


def main():
    st.title("💬 Wren AI Cloud API Demo - SQL Generation")

    if "api_key" not in st.session_state or "project_id" not in st.session_state:
        st.error("Please enter your API Key and Project ID in the sidebar of Home page to get started.")
        return
    if not st.session_state.api_key or not st.session_state.project_id:
        st.error("Please enter your API Key and Project ID in the sidebar of Home page to get started.")
        return
    
    api_key = st.session_state.api_key
    project_id = st.session_state.project_id

    st.markdown('Using APIs: [Ask](https://wrenai.readme.io/reference/post_ask-1), [Run SQL](https://wrenai.readme.io/reference/cloud_post_run-sql)')

    # Sidebar for API configuration
    with st.sidebar:
        st.header("🔧 Configuration")
        language = st.text_input(
            "Language",
            "English",
            help="Language of the response. Default is English."
        )
        sample_size = st.slider(
            "Sample Size", 
            min_value=100, 
            max_value=10000, 
            value=1000,
            step=100,
            help="Number of data points to include in results"
        )
        
    # Initialize chat history
    if "qa_messages" not in st.session_state:
        st.session_state.qa_messages = []
    if "qa_thread_id" not in st.session_state:
        st.session_state.qa_thread_id = ""

    # Display chat history
    for message in st.session_state.qa_messages:
        with st.chat_message(message["role"]):
            if message["role"] == "user":
                st.write(message["content"])
            else:
                st.write(message["content"])
                if "sql" in message:
                    with st.expander("🔍 Generated SQL Query", expanded=False):
                        st.code(format_sql(message["sql"]), language="sql")
                        
                        # Add button to run SQL
                        if st.button("🔄 Run SQL Query", key=f"run_sql_{message.get('message_id', 'unknown')}"):
                            with st.spinner("Executing SQL query..."):
                                sql_result, error = run_sql(api_key, project_id, message["sql"], st.session_state.qa_thread_id, sample_size)
                            
                            if sql_result:
                                data = sql_result.get("records", [])                                
                                if data:
                                    # Convert to DataFrame for better display
                                    df = pd.DataFrame(data)
                                    st.success("SQL query executed successfully!")
                                    st.dataframe(df, use_container_width=True)
                                else:
                                    st.info("Query executed but returned no data.")
                            else:
                                st.error(f"Error executing SQL: {error}")
                
                if "sql_results" in message:
                    st.subheader("🔍 Query Results")
                    if message["sql_results"]:
                        st.dataframe(message["sql_results"], use_container_width=True)
                    else:
                        st.info("No results returned from the query.")

    # Chat input
    if prompt := st.chat_input("Ask a question about your data..."):
        # Add user message to chat history
        st.session_state.qa_messages.append({"role": "user", "content": prompt})
        
        # Display user message
        with st.chat_message("user"):
            st.write(prompt)

        # Generate response using ask API
        with st.chat_message("assistant"):
            with st.spinner("Generating answer..."):
                ask_response, error = ask(api_key, project_id, prompt, st.session_state.qa_thread_id, sample_size=sample_size, language=language)
            
            if ask_response:
                answer = ask_response.get("summary", "")
                sql_query = ask_response.get("sql", "")
                st.session_state.qa_thread_id = ask_response.get("threadId", "")
                
                if answer:
                    st.toast("Answer generated successfully!", icon="🎉")
                    
                    # Create unique message ID
                    message_id = len(st.session_state.qa_messages)
                    
                    # Store the response
                    assistant_message = {
                        "role": "assistant",
                        "content": answer,
                        "message_id": message_id
                    }
                    
                    if sql_query:
                        assistant_message["sql"] = sql_query
                    
                    st.session_state.qa_messages.append(assistant_message)
                    st.write(answer)

                    # Display SQL query if available
                    if sql_query:
                        with st.expander("🔍 Generated SQL Query", expanded=False):
                            st.code(format_sql(sql_query), language="sql")
                            
                            # Add button to run SQL
                            if st.button("🔄 Run SQL Query", key=f"run_sql_{message_id}"):
                                with st.spinner("Executing SQL query..."):
                                    sql_result, error = run_sql(api_key, project_id, sql_query, st.session_state.qa_thread_id, sample_size)
                                
                                if sql_result:
                                    data = sql_result.get("records", [])                                    
                                    if data:
                                        # Convert to DataFrame for better display
                                        df = pd.DataFrame(data)
                                        st.success("SQL query executed successfully!")
                                        st.dataframe(df, use_container_width=True)
                                    else:
                                        st.info("Query executed but returned no data.")
                                else:
                                    st.error(f"Error executing SQL: {error}")
                else:
                    st.toast("No answer was generated. Please try rephrasing your question.", icon="🤔")
                    assistant_message = {
                        "role": "assistant",
                        "content": "I couldn't generate an answer for your question. Please try rephrasing it or make sure it's related to your data."
                    }
                    st.session_state.qa_messages.append(assistant_message)
            else:
                st.toast(f"Error generating answer: {error}", icon="🤔")
                assistant_message = {
                    "role": "assistant", 
                    "content": "Sorry, I couldn't process your request. Please check your API credentials and try again."
                }
                st.session_state.qa_messages.append(assistant_message)

    # Clear chat button
    if st.sidebar.button("🧹 Clear Chat History"):
        st.session_state.qa_messages = []
        st.session_state.qa_thread_id = ""
        st.rerun()


if __name__ == "__main__":
    main()