my-project-public

repository

loading code, commits, and activity

repositories

loading repo index

#1	import csv
#2	import queue
#3	import threading
#4	from io import StringIO
#5
#6	import requests
#7	import streamlit as st
#8
#9	from embedchain import App
#10	from embedchain.config import BaseLlmConfig
#11	from embedchain.helpers.callbacks import StreamingStdOutCallbackHandlerYield, generate
#12
#13
#14	@st.cache_resource
#15	def sadhguru_ai():
#16	app = App()
#17	return app
#18
#19
#20	# Function to read the CSV file row by row
#21	def read_csv_row_by_row(file_path):
#22	with open(file_path, mode="r", newline="", encoding="utf-8") as file:
#23	csv_reader = csv.DictReader(file)
#24	for row in csv_reader:
#25	yield row
#26
#27
#28	@st.cache_resource
#29	def add_data_to_app():
#30	app = sadhguru_ai()
#31	url = "https://gist.githubusercontent.com/deshraj/50b0597157e04829bbbb7bc418be6ccb/raw/95b0f1547028c39691f5c7db04d362baa597f3f4/data.csv" # noqa:E501
#32	response = requests.get(url)
#33	csv_file = StringIO(response.text)
#34	for row in csv.reader(csv_file):
#35	if row and row[0] != "url":
#36	app.add(row[0], data_type="web_page")
#37
#38
#39	app = sadhguru_ai()
#40	add_data_to_app()
#41
#42	assistant_avatar_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/2/21/Sadhguru-Jaggi-Vasudev.jpg/640px-Sadhguru-Jaggi-Vasudev.jpg" # noqa: E501
#43
#44
#45	st.title("🙏 Sadhguru AI")
#46
#47	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
#48	st.markdown(styled_caption, unsafe_allow_html=True) # noqa: E501
#49
#50	if "messages" not in st.session_state:
#51	st.session_state.messages = [
#52	{
#53	"role": "assistant",
#54	"content": """
#55	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.
#56	""", # noqa: E501
#57	}
#58	]
#59
#60	for message in st.session_state.messages:
#61	role = message["role"]
#62	with st.chat_message(role, avatar=assistant_avatar_url if role == "assistant" else None):
#63	st.markdown(message["content"])
#64
#65	if prompt := st.chat_input("Ask me anything!"):
#66	with st.chat_message("user"):
#67	st.markdown(prompt)
#68	st.session_state.messages.append({"role": "user", "content": prompt})
#69
#70	with st.chat_message("assistant", avatar=assistant_avatar_url):
#71	msg_placeholder = st.empty()
#72	msg_placeholder.markdown("Thinking...")
#73	full_response = ""
#74
#75	q = queue.Queue()
#76
#77	def app_response(result):
#78	config = BaseLlmConfig(stream=True, callbacks=[StreamingStdOutCallbackHandlerYield(q)])
#79	answer, citations = app.chat(prompt, config=config, citations=True)
#80	result["answer"] = answer
#81	result["citations"] = citations
#82
#83	results = {}
#84	thread = threading.Thread(target=app_response, args=(results,))
#85	thread.start()
#86
#87	for answer_chunk in generate(q):
#88	full_response += answer_chunk
#89	msg_placeholder.markdown(full_response)
#90
#91	thread.join()
#92	answer, citations = results["answer"], results["citations"]
#93	if citations:
#94	full_response += "\n\nSources:\n"
#95	sources = list(set(map(lambda x: x[1]["url"], citations)))
#96	for i, source in enumerate(sources):
#97	full_response += f"{i+1}. {source}\n"
#98
#99	msg_placeholder.markdown(full_response)
#100	st.session_state.messages.append({"role": "assistant", "content": full_response})
#101

z6Mkq5mY3JWtxoxUobWcfNHm7AkRubgSWEZTkBVqZXJviFZ5/my-project-public