import csv
import queue
import threading
from io import StringIO

import requests
import streamlit as st

from embedchain import Pipeline as App
from embedchain.config import BaseLlmConfig
from embedchain.helpers.callbacks import (StreamingStdOutCallbackHandlerYield,
                                          generate)


@st.cache_resource
def sadhguru_ai():
    app = App()
    return app


# Function to read the CSV file row by row
def read_csv_row_by_row(file_path):
    with open(file_path, mode="r", newline="", encoding="utf-8") as file:
        csv_reader = csv.DictReader(file)
        for row in csv_reader:
            yield row


@st.cache_resource
def add_data_to_app():
    app = sadhguru_ai()
    url = "https://gist.githubusercontent.com/deshraj/50b0597157e04829bbbb7bc418be6ccb/raw/95b0f1547028c39691f5c7db04d362baa597f3f4/data.csv"  # noqa:E501
    response = requests.get(url)
    csv_file = StringIO(response.text)
    for row in csv.reader(csv_file):
        if row and row[0] != "url":
            app.add(row[0], data_type="web_page")


app = sadhguru_ai()
add_data_to_app()

assistant_avatar_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/2/21/Sadhguru-Jaggi-Vasudev.jpg/640px-Sadhguru-Jaggi-Vasudev.jpg"  # noqa: E501


st.title("🙏 Sadhguru AI")

styled_caption = '<p style="font-size: 17px; color: #aaa;">🚀 An <a href="https://github.com/embedchain/embedchain">Embedchain</a> app powered with Sadhguru\'s wisdom!</p>'  # noqa: E501
st.markdown(styled_caption, unsafe_allow_html=True)  # noqa: E501

if "messages" not in st.session_state:
    st.session_state.messages = [
        {
            "role": "assistant",
            "content": """
                Hi, I'm Sadhguru AI! I'm a mystic, yogi, visionary, and spiritual master. I'm here to answer your questions about life, the universe, and everything.
            """,  # noqa: E501
        }
    ]

for message in st.session_state.messages:
    role = message["role"]
    with st.chat_message(role, avatar=assistant_avatar_url if role == "assistant" else None):
        st.markdown(message["content"])

if prompt := st.chat_input("Ask me anything!"):
    with st.chat_message("user"):
        st.markdown(prompt)
        st.session_state.messages.append({"role": "user", "content": prompt})

    with st.chat_message("assistant", avatar=assistant_avatar_url):
        msg_placeholder = st.empty()
        msg_placeholder.markdown("Thinking...")
        full_response = ""

        q = queue.Queue()

        def app_response(result):
            config = BaseLlmConfig(stream=True, callbacks=[StreamingStdOutCallbackHandlerYield(q)])
            answer, citations = app.chat(prompt, config=config, citations=True)
            result["answer"] = answer
            result["citations"] = citations

        results = {}
        thread = threading.Thread(target=app_response, args=(results,))
        thread.start()

        for answer_chunk in generate(q):
            full_response += answer_chunk
            msg_placeholder.markdown(full_response)

        thread.join()
        answer, citations = results["answer"], results["citations"]
        if citations:
            full_response += "\n\n**Sources**:\n"
            for i, citations in enumerate(citations):
                full_response += f"{i+1}. {citations[1]}\n"

        msg_placeholder.markdown(full_response)
        st.session_state.messages.append({"role": "assistant", "content": full_response})