from langchain.prompts import PromptTemplate
from langchain.output_parsers import PydanticOutputParser
from langchain.chat_models import ChatOpenAI

from llama_index import VectorStoreIndex, ServiceContext, StorageContext,  download_loader, SimpleDirectoryReader
from llama_index.vector_stores import FaissVectorStore
from llama_index.tools import QueryEngineTool, ToolMetadata
from llama_index.query_engine import SubQuestionQueryEngine
from llama_index.embeddings import OpenAIEmbedding
from llama_index.schema import Document
from llama_index.node_parser import UnstructuredElementNodeParser
from llama_index.llms import OpenAI

import streamlit as st
import os
import faiss
import time


st.set_page_config(page_title="Yield Case Analyzer", page_icon=":card_index_dividers:", initial_sidebar_state="expanded", layout="wide")

st.title(":card_index_dividers: Yield Case Analyzer")
st.info("""
Begin by uploading the case report in PDF format. Afterward, click on 'Process Document'. Once the document has been processed. You can enter question and click send, system will answer your question.
""")


def get_model(model_name):
    OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")
    if model_name == "openai":
        model = ChatOpenAI(openai_api_key=OPENAI_API_KEY, model_name="gpt-3.5-turbo")
    return model

def get_vector_index(docs, vector_store):
    print(docs)
    llm = get_model("openai")
    if vector_store == "faiss":
        d = 1536
        faiss_index = faiss.IndexFlatL2(d)
        vector_store = FaissVectorStore(faiss_index=faiss_index)
        storage_context = StorageContext.from_defaults(vector_store=vector_store)
        # embed_model = OpenAIEmbedding()
        # service_context = ServiceContext.from_defaults(embed_model=embed_model)
        service_context = ServiceContext.from_defaults(llm=llm)
        index = VectorStoreIndex(docs,
            service_context=service_context,
            storage_context=storage_context
        )
    elif vector_store == "simple":
        index = VectorStoreIndex.from_documents(docs)


    return index



def generate_insight(engine, search_string):

    with open("prompts/main.prompt", "r") as f:
        template = f.read()

    prompt_template = PromptTemplate(
        template=template,
        input_variables=['search_string']
    )

    formatted_input = prompt_template.format(search_string=search_string)
    print(formatted_input)
    response = engine.query(formatted_input)
    return response.response


def get_query_engine(engine):
    llm = get_model("openai")
    service_context = ServiceContext.from_defaults(llm=llm)

    query_engine_tools = [
        QueryEngineTool(
            query_engine=engine,
            metadata=ToolMetadata(
                name="Alert Report",
                description=f"Provides information about the alerts from alerts files uploaded.",
            ),
        ),
    ]


    s_engine = SubQuestionQueryEngine.from_defaults(
        query_engine_tools=query_engine_tools,
        service_context=service_context
    )
    return s_engine


if "process_doc" not in st.session_state:
        st.session_state.process_doc = False


OPENAI_API_KEY = "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"
os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY


if OPENAI_API_KEY:
    files_uploaded = st.sidebar.file_uploader("Upload the case report in PDF format", type="pptx")
    st.sidebar.info("""
    Example pdf reports you can upload here:
    """)

    if st.sidebar.button("Process Document"):
        with st.spinner("Processing Document..."):

        data_dir = "./data"
        if not os.path.exists(data_dir):
            os.makedirs(data_dir)

        for file in files_uploaded:
            print(f'file named {file.name}')
            fname=f'{data_dir}/{file.name}'
            with open(fname, 'wb') as f:
                f.write(file.read())

        def fmetadata(dummy: str): return {"file_path": ""}

        PptxReader = download_loader("PptxReader")
        loader =  SimpleDirectoryReader(input_dir=data_dir, file_extractor={".pptx": PptxReader(),}, file_metadata=fmetadata)

        documents = loader.load_data()
        for doc in documents:
            doc.metadata["file_path"]=""

        st.session_state.index = get_vector_index(documents, vector_store="faiss")
        #st.session_state.index = get_vector_index(documents, vector_store="simple")
        st.session_state.process_doc = True
        st.toast("Document Processsed!")

    #st.session_state.process_doc = True

    if st.session_state.process_doc:
        search_text = st.text_input("Enter your question")
        if st.button("Submit"):
            engine = get_query_engine(st.session_state.index.as_query_engine(similarity_top_k=3))
            start_time = time.time()

            st.write("Alert search result...")
            response = generate_insight(engine, search_text)
            st.write(response)
            #st.session_state["end_time"] = "{:.2f}".format((time.time() - start_time))

            st.toast("Report Analysis Complete!")

        #if st.session_state.end_time:
        #    st.write("Report Analysis Time: ", st.session_state.end_time, "s")