import os
from dotenv import load_dotenv
import streamlit as st
import webchat
import utils
# URL of the hosted LLMs is hardcoded because at this time all LLMs share the same endpoint
url = "https://us-south.ml.cloud.ibm.com"
# These global variables will be updated in get_credentials() function
watsonx_project_id = ""
api_key = ""
def main():
    utils.get_credentials()
    st.set_page_config(layout="wide", page_title="RAG Web Demo", page_icon="")
    utils.load_css("styles.css")
    # Streamlit app title with style
    st.markdown("""
        <div class="menu-bar">
            <h1>IBM watsonx.ai - webchat</h1>
        </div>
        <div style="margin-top: 20px;"><p>Insert the website you want to chat with and ask your question.</p></div>
        
    """, unsafe_allow_html=True)
    # Sidebar for settings
    st.sidebar.header("Settings")
    st.sidebar.markdown("Insert your credentials of [IBM Cloud](https://cloud.ibm.com/login) for watsonx.ai \n The data is not saved  in th server. Your data  is secured.", unsafe_allow_html=True)
    st.sidebar.markdown("<hr>", unsafe_allow_html=True)
    api_key_input = st.sidebar.text_input("API Key", api_key, type="password")
    project_id_input = st.sidebar.text_input("Project ID", watsonx_project_id)
    # Update credentials if provided by the user
    if api_key_input:
        globals()["api_key"] = api_key_input
    if project_id_input:
        globals()["watsonx_project_id"] = project_id_input
    # Main input area
    user_url = st.text_input('Provide a URL')
    # UI component to enter the question
    question = st.text_area('Question', height=100)
    button_clicked = st.button("Answer the question")
    st.markdown("<hr>", unsafe_allow_html=True)
    st.subheader("Response")
    collection_name="base"
    if globals()["api_key"] and globals()["watsonx_project_id"]:
        # Provide a unique name for this website (lower case). Use the same name for the same URL to avoid loading data multiple times.
        #collection_name = utils.create_collection_name(user_url)
        if button_clicked and user_url:
            # Invoke the LLM when the button is clicked
            response = webchat.answer_questions_from_web(api_key, watsonx_project_id, user_url, question, collection_name)
            st.write(response)

    else:
        st.warning("Please provide API Key and Project ID in the sidebar.")
  
    # Cleaning Vector Database
    st.sidebar.markdown("<hr>", unsafe_allow_html=True)
    st.sidebar.header("Memory")
    clean_button_clicked = st.sidebar.button("Clean Memory")
    if clean_button_clicked :
        if collection_name:  # Check if collection_name is defined and not empty
            utils.clear_collection(collection_name)
            st.sidebar.success("Memory cleared successfully!")
            #st.sidebar.markdown(collection_name, unsafe_allow_html=True)
            print("Memory cleared successfully!")
        else:
            st.sidebar.error("Collection name is not defined or empty.")

if __name__ == "__main__":
    main()