import streamlit as st
import os
from io import StringIO
from llama_index.llms import HuggingFaceInferenceAPI
from llama_index.embeddings import HuggingFaceInferenceAPIEmbedding
from llama_index import ServiceContext, VectorStoreIndex
from llama_index.schema import Document
import uuid
from llama_index.vector_stores.types import MetadataFilters, ExactMatchFilter

inference_api_key = st.secrets["INFRERENCE_API_TOKEN"]

embed_model_name = st.text_input(
    'Embed Model name', "Gooly/gte-small-en-fine-tuned-e-commerce")

llm_model_name = st.text_input(
    'Embed Model name', "mistralai/Mistral-7B-Instruct-v0.2")

query = st.text_input(
    'Query', "What is the price of the product?")

html_file = st.file_uploader("Upload a html file", type=["html"])

if st.button('Start Pipeline'):
    if html_file is not None and embed_model_name is not None and llm_model_name is not None and query is not None:
        st.write('Running Pipeline')
        llm = HuggingFaceInferenceAPI(
            model_name=llm_model_name, token=inference_api_key)

        embed_model = HuggingFaceInferenceAPIEmbedding(
            model_name=embed_model_name,
            token=inference_api_key,
            model_kwargs={"device": ""},
            encode_kwargs={"normalize_embeddings": True},
        )

        service_context = ServiceContext.from_defaults(
            embed_model=embed_model, llm=llm)

        stringio = StringIO(html_file.getvalue().decode("utf-8"))
        string_data = stringio.read()
        with st.expander("Uploaded HTML"):
            st.write(string_data)

        document_id = str(uuid.uuid4())

        document = Document(text=string_data)
        document.metadata["id"] = document_id
        documents = [document]

        filters = MetadataFilters(
            filters=[ExactMatchFilter(key="id", value=document_id)])

        index = VectorStoreIndex.from_documents(
            documents, show_progress=True, metadata={"source": "HTML"}, service_context=service_context)

        retriever = index.as_retriever()

        ranked_nodes = retriever.retrieve(
            query)

        with st.expander("Ranked Nodes"):
            for node in ranked_nodes:
                st.write(node.node.get_content(), "-> Score:", node.score)

        query_engine = index.as_query_engine(
            filters=filters, service_context=service_context)

        response = query_engine.query(query)

        st.write(response)

    else:
        st.error('Please fill in all the fields')
else:
    st.write('Press start to begin')

# if html_file is not None:
#     stringio = StringIO(html_file.getvalue().decode("utf-8"))
#     string_data = stringio.read()
#     with st.expander("Uploaded HTML"):
#         st.write(string_data)

#     document_id = str(uuid.uuid4())

#     document = Document(text=string_data)
#     document.metadata["id"] = document_id
#     documents = [document]

#     filters = MetadataFilters(
#         filters=[ExactMatchFilter(key="id", value=document_id)])

#     index = VectorStoreIndex.from_documents(
#         documents, show_progress=True, metadata={"source": "HTML"}, service_context=service_context)

#     retriever = index.as_retriever()

#     ranked_nodes = retriever.retrieve(
#         "Get me all the information about the product")

#     with st.expander("Ranked Nodes"):
#         for node in ranked_nodes:
#             st.write(node.node.get_content(), "-> Score:", node.score)

#     query_engine = index.as_query_engine(
#         filters=filters, service_context=service_context)

#     response = query_engine.query(
#         "Get me all the information about the product")

#     st.write(response)