import streamlit as st
import latex2markdown
from langchain.docstore.document import Document
import chromadb
from chromadb.config import Settings
import load_model
from load_model import load_embedding
from load_vectors import load_from_file, load_and_split, create_and_add, load_from_web
persist_directory = load_model.persist_directory

def format_document(document: Document):
    """TODO: Implement a nice style"""
    return document.dict()

def format_result_set(result):
    st.write(latex2markdown.LaTeX2Markdown(result["result"]).to_markdown())

    agree = st.checkbox('Show source documents')
    source_documents = result["source_documents"]
    if agree:
        st.write('Source Documents:')
        for document in source_documents:
            st.write(format_document(document))

@st.cache_resource
def get_chroma_client():
    return chromadb.Client(Settings(chroma_db_impl="duckdb+parquet",
                                    persist_directory=persist_directory
                                ))
#@st.cache_data
def retrieve_collections():
    client = get_chroma_client()
    all_collections = client.list_collections()
    collections = tuple( [{'name': collection.name, 'model_name': collection.metadata['model_name'], "metadata": collection.metadata} for collection in all_collections] )
    return collections

def load_files():
    
    client = get_chroma_client()

    option = st.radio(
        "",
        options=["Add Documents", "Start new collection"],
    )

    if option == "Add Documents":
        collections = retrieve_collections()
        selected_collection = st.selectbox(
            'Add to exsisting collection or create a new one',
            collections )
        if st.button('Delete Collection (⚠️ This is destructive and not reversible)'):
            client.delete_collection(name=selected_collection["name"])
            #retrieve_collections.clear()
            collections = retrieve_collections()

        if selected_collection:
            st.write("Selected Vectorstore:", selected_collection)
        option = st.radio(
            "",
            options=["Upload Files from Local", "Upload Files from Web"],
        )
        if option == "Upload Files from Local":
            st.write('Source Documents:')
            uploaded_files = st.file_uploader("Choose a PDF file", accept_multiple_files=True)
            chunk_size = st.text_area('chunk Size:', 1000)

            if st.button('Upload'):
                docs = load_from_file(uploaded_files)
                sub_docs = load_and_split(docs, chunk_size=int(chunk_size))
                vec1 = create_and_add(selected_collection["name"], sub_docs, selected_collection['model_name'], selected_collection['metadata'])
                st.write("Upload succesful")
        else:
            st.write('Urls of Source Documents (Comma separated):')
            urls = chunk_size = st.text_area('Urls:', '')
            chunk_size = st.text_area('chunk Size:', 1000)
            urls = urls.replace(",", "" ).replace('"', "" ).split(',')

            if st.button('Upload'):
                docs = load_from_web(urls)     
                sub_docs = load_and_split(docs, chunk_size=int(chunk_size))
                vec2 = create_and_add(selected_collection["name"], sub_docs, selected_collection['model_name'], selected_collection['metadata'])
                st.write("Upload succesful")
    else:
        collection = st.text_area('Name of your new collection:', '')
        model_name = st.text_area('Choose the embedding function:', "hkunlp/instructor-large")
        if st.button('Create'):
            if len(collection)>3:
                ef = load_embedding(model_name)
                metadata= {"loaded_docs":[], "Subject":"Terms Example", "model_name": ef.model_name}
                client.create_collection(collection, embedding_function=ef, metadata=metadata) 
                # retrieve_collections.clear()
                st.write("Collection " +collection+" succesfully created.")