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import csv
import queue
import threading
from io import StringIO
import requests
import streamlit as st
from embedchain import 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"
sources = list(set(map(lambda x: x[1]["url"], citations)))
for i, source in enumerate(sources):
full_response += f"{i+1}. {source}\n"
msg_placeholder.markdown(full_response)
st.session_state.messages.append({"role": "assistant", "content": full_response})
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