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#1	import queue
#2
#3	import streamlit as st
#4
#5	from embedchain import App
#6	from embedchain.config import BaseLlmConfig
#7	from embedchain.helpers.callbacks import StreamingStdOutCallbackHandlerYield, generate
#8
#9
#10	@st.cache_resource
#11	def unacademy_ai():
#12	app = App()
#13	return app
#14
#15
#16	app = unacademy_ai()
#17
#18	assistant_avatar_url = "https://cdn-images-1.medium.com/v2/resize:fit:1200/1*LdFNhpOe7uIn-bHK9VUinA.jpeg"
#19
#20	st.markdown(f"# <img src='{assistant_avatar_url}' width={35} /> Unacademy UPSC AI", unsafe_allow_html=True)
#21
#22	styled_caption = """
#23	<p style="font-size: 17px; color: #aaa;">
#24	🚀 An <a href="https://github.com/embedchain/embedchain">Embedchain</a> app powered with Unacademy\'s UPSC data!
#25	</p>
#26	"""
#27	st.markdown(styled_caption, unsafe_allow_html=True)
#28
#29	with st.expander(":grey[Want to create your own Unacademy UPSC AI?]"):
#30	st.write(
#31	"""
#32	```bash
#33	pip install embedchain
#34	```
#35
#36	```python
#37	from embedchain import App
#38	unacademy_ai_app = App()
#39	unacademy_ai_app.add(
#40	"https://unacademy.com/content/upsc/study-material/plan-policy/atma-nirbhar-bharat-3-0/",
#41	data_type="web_page"
#42	)
#43	unacademy_ai_app.chat("What is Atma Nirbhar 3.0?")
#44	```
#45
#46	For more information, checkout the [Embedchain docs](https://docs.embedchain.ai/get-started/quickstart).
#47	"""
#48	)
#49
#50	if "messages" not in st.session_state:
#51	st.session_state.messages = [
#52	{
#53	"role": "assistant",
#54	"content": """Hi, I'm Unacademy UPSC AI bot, who can answer any questions related to UPSC preparation.
#55	Let me help you prepare better for UPSC.\n
#56	Sample questions:
#57	- What are the subjects in UPSC CSE?
#58	- What is the CSE scholarship price amount?
#59	- What are different indian calendar forms?
#60	""",
#61	}
#62	]
#63
#64	for message in st.session_state.messages:
#65	role = message["role"]
#66	with st.chat_message(role, avatar=assistant_avatar_url if role == "assistant" else None):
#67	st.markdown(message["content"])
#68
#69	if prompt := st.chat_input("Ask me anything!"):
#70	with st.chat_message("user"):
#71	st.markdown(prompt)
#72	st.session_state.messages.append({"role": "user", "content": prompt})
#73
#74	with st.chat_message("assistant", avatar=assistant_avatar_url):
#75	msg_placeholder = st.empty()
#76	msg_placeholder.markdown("Thinking...")
#77	full_response = ""
#78
#79	q = queue.Queue()
#80
#81	def app_response(result):
#82	llm_config = app.llm.config.as_dict()
#83	llm_config["callbacks"] = [StreamingStdOutCallbackHandlerYield(q=q)]
#84	config = BaseLlmConfig(**llm_config)
#85	answer, citations = app.chat(prompt, config=config, citations=True)
#86	result["answer"] = answer
#87	result["citations"] = citations
#88
#89	results = {}
#90
#91	for answer_chunk in generate(q):
#92	full_response += answer_chunk
#93	msg_placeholder.markdown(full_response)
#94
#95	answer, citations = results["answer"], results["citations"]
#96
#97	if citations:
#98	full_response += "\n\nSources:\n"
#99	sources = list(set(map(lambda x: x[1], citations)))
#100	for i, source in enumerate(sources):
#101	full_response += f"{i+1}. {source}\n"
#102
#103	msg_placeholder.markdown(full_response)
#104	st.session_state.messages.append({"role": "assistant", "content": full_response})
#105

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