import streamlit as st
from tempfile import NamedTemporaryFile

import pprint
import google.generativeai as palm
import os
from dotenv import load_dotenv, find_dotenv
from langchain.embeddings import GooglePalmEmbeddings
from langchain.llms import GooglePalm

from langchain.document_loaders import UnstructuredURLLoader  #load urls into docoument-loader
from langchain.chains.question_answering import load_qa_chain
from langchain.indexes import VectorstoreIndexCreator #vectorize db index with chromadb
from langchain.text_splitter import CharacterTextSplitter #text splitter
from langchain.chains import RetrievalQA
from langchain.document_loaders import UnstructuredPDFLoader  #load pdf
from langchain.agents import create_pandas_dataframe_agent

import pandas as pd
import numpy as np
import pprint

isPswdValid = False
try:
    pswdVal = st.experimental_get_query_params()['pwd'][0]
    if pswdVal==st.secrets["PSWD"]:
        isPswdValid = True
except:
    pass

if not isPswdValid:
    st.write("Invalid Password")
else:
    radioButtonList = ["E-commerce CSV (https://www.kaggle.com/datasets/mervemenekse/ecommerce-dataset)",
    "Upload my own CSV",
    "Upload my own PDF",
    "URL Chat with Google's Latest Earnings (https://abc.xyz/investor/)",
    "Enter my own URL"]

    # Add some designs to the radio buttons
    st.markdown("""
    <style>
    .stRadio {
      padding: 10px;
      border-radius: 5px;
      background-color: #f5f5f5;
    }

    .stRadio input[type="radio"] {
      position: absolute;
      opacity: 0;
      cursor: pointer;
    }

    .stRadio label {
      display: flex;
      justify-content: center;
      align-items: center;
      cursor: pointer;
      font-size: 16px;
      color: #333;
    }

    .stRadio label:hover {
      color: #000;
    }

    .stRadio.st-selected input[type="radio"] ~ label {
      color: #000;
      background-color: #d9d9d9;
    }
    </style>
    """, unsafe_allow_html=True)

    genre = st.radio(
        "Choose dataset to finetune", radioButtonList, index=0
    )

    # Initialize language model
    load_dotenv(find_dotenv()) # read local .env file
    api_key = st.secrets["PALM_API_KEY"] # put your API key here
    os.environ["GOOGLE_API_KEY"] = st.secrets["PALM_API_KEY"]
    palm.configure(api_key=api_key)
    llm = GooglePalm()
    llm.temperature = 0.1

    pdfCSVURLText = ""
    if genre==radioButtonList[0]:
        pdfCSVURLText = "CSV"
        exampleQuestion = "Question1: What was the most sold item? Question2: What was the most common payment?"
        dataDF = pd.read_csv('EcommerceDataset.csv', encoding= 'unicode_escape')
        # st.write('You selected comedy.')
        # else:
        # st.write(f'''Password streamlit app: {st.secrets["PSWD"]}''')
    elif genre==radioButtonList[1]:
        pdfCSVURLText = "CSV"
        exampleQuestion = "What are the data columns?"
    elif genre==radioButtonList[2]:
        pdfCSVURLText = "PDF"
        exampleQuestion = "Can you summarize the contents?"
    elif genre==radioButtonList[3]:
        pdfCSVURLText = "URL"
        exampleQuestion = "What is Google's latest earnings?"
        urls = ['https://abc.xyz/investor/']
        loader = [UnstructuredURLLoader(urls=urls)]
        index = VectorstoreIndexCreator(
                embedding=GooglePalmEmbeddings(),
                text_splitter=CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)).from_loaders(loader)

        chain = RetrievalQA.from_chain_type(llm=llm,
                                    chain_type="stuff",
                                    retriever=index.vectorstore.as_retriever(),
                                    input_key="question")
    elif genre==radioButtonList[4]:
        pdfCSVURLText = "URL"
        exampleQuestion = "Can you summarize the contents?"

    isCustomURL = genre==radioButtonList[4]
    urlInput = st.text_input('Enter your own URL', '', placeholder="Type your URL here (e.g. https://abc.xyz/investor/)", disabled=not isCustomURL)

    isCustomPDF = genre==radioButtonList[1] or genre==radioButtonList[2]
    uploaded_file = st.file_uploader(f"Upload your own {pdfCSVURLText} here", type=pdfCSVURLText.lower(), disabled=not isCustomPDF)
    uploadedFilename = ""
    if uploaded_file is not None:
        with NamedTemporaryFile(dir='.', suffix=f'.{pdfCSVURLText.lower()}') as f:
            f.write(uploaded_file.getbuffer())
            uploadedFilename = f.name
            if genre==radioButtonList[1]: # Custom CSV Upload
                dataDF = pd.read_csv(uploadedFilename, encoding= 'unicode_escape')
            elif genre==radioButtonList[2]: # Custom PDF Upload
                pdf_loaders = [UnstructuredPDFLoader(uploadedFilename)]
                pdf_index = VectorstoreIndexCreator(
                        embedding=GooglePalmEmbeddings(),
                        text_splitter=CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)).from_loaders(pdf_loaders)
                pdf_chain = RetrievalQA.from_chain_type(llm=llm,
                                            chain_type="stuff",
                                            retriever=pdf_index.vectorstore.as_retriever(),
                                            input_key="question")

    enableChatBox = False
    if genre==radioButtonList[0]: # E-commerce CSV
        enableChatBox = True
    elif genre==radioButtonList[1]: # Custom CSV Upload
        enableChatBox = uploadedFilename[-4:]==".csv"
    elif genre==radioButtonList[2]: # Custom PDF Upload
        enableChatBox = uploadedFilename[-4:]==".pdf"
    elif genre==radioButtonList[3]: # Google Alphabet URL Earnings Report
        enableChatBox = True
    elif genre==radioButtonList[4]: # Custom URL
        enableChatBox = True

    chatTextStr = st.text_input(f'Ask me anything about this {pdfCSVURLText}', '', placeholder=f"Type here (e.g. {exampleQuestion})", disabled=not enableChatBox)
    chatWithPDFButton = "CLICK HERE TO START CHATTING"
    if st.button(chatWithPDFButton, disabled=not enableChatBox and not chatTextStr): #  Button Cliked


        if genre==radioButtonList[0]: # E-commerce CSV
            # Initializing the agent
            agent = create_pandas_dataframe_agent(llm, dataDF, verbose=False)
            answer = agent.run(chatTextStr)
            st.write(answer)

        elif genre==radioButtonList[1]: # Custom CSV Upload
            # Initializing the agent
            agent = create_pandas_dataframe_agent(llm, dataDF, verbose=False)
            answer = agent.run(chatTextStr)
            st.write(answer)

        elif genre==radioButtonList[2]: # Custom PDF Upload
            pdf_answer = pdf_chain.run(chatTextStr)
            st.write(pdf_answer)

        elif genre==radioButtonList[3]: # Google Alphabet URL Earnings Report
            answer = chain.run(chatTextStr)
            st.write(answer)

        elif genre==radioButtonList[4]: # Custom URL
            urls = [urlInput]
            loader = [UnstructuredURLLoader(urls=urls)]
            index = VectorstoreIndexCreator(
                    embedding=GooglePalmEmbeddings(),
                    text_splitter=CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)).from_loaders(loader)

            chain = RetrievalQA.from_chain_type(llm=llm,
                                        chain_type="stuff",
                                        retriever=index.vectorstore.as_retriever(),
                                        input_key="question")
            answer = chain.run(chatTextStr)
            st.write(answer)