mem0/examples/sadhguru-ai/app.py

import csv
import queue
import threading

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()
    file_path = "data.csv"
    for row in read_csv_row_by_row(file_path):
        app.add(row["url"], 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})