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from typing import Set
from backend.core import run_llm
import streamlit as st
from streamlit_chat import message
from PIL import Image
from io import BytesIO
import base64
# def add_bg_from_local(image_file):
# with open(image_file, "rb") as image_file:
# encoded_string = base64.b64encode(image_file.read())
# st.markdown(
# f"""
# <style>
# .stApp {{
# background-image: url(data:{"jpeg"};base64,{encoded_string.decode()});
# background-size: cover
# }}
# </style>
# """,
# unsafe_allow_html=True
# )
# background_image = "bg2.jpeg"
# add_bg_from_local(background_image)
st.header("LangChain 🦜🔗 Documentation - Helper ChatBot")
# profile_image = Image.open("langchain background removed.png")
# st.sidebar.image(profile_image, width=200)
st.markdown(
"""
<style>
[data-testid=stSidebar] [data-testid=stImage]{
text-align: center;
display: block;
margin-left: auto;
margin-right: auto;
width: 100%;
}
</style>
""", unsafe_allow_html=True
)
with st.sidebar:
st.image("langchain background removed.png", width = 200)
st.sidebar.markdown(
"""
# LangChain Helper Bot 🤖
Welcome to the LangChain Helper Bot!
**What does it offer ?**
🚀 Easy scraping of LangChain documentation.
📚 Instead of going through tedious LangChain documentation, just submit your query here about any LangChain topic, and you'll get the answer!
---
**How to use ?**
Just type your LangChain-related query in the input box on the main page, and the bot will fetch precise information for you.
"""
)
with st.sidebar:
st.caption(
"""The official documentation of langchain can be found at
https://python.langchain.com/docs/get_started
"""
)
st.markdown("---")
st.markdown(
'<h6>Made in  <img src="https://streamlit.io/images/brand/streamlit-mark-color.png" alt="Streamlit logo" height="16">  by <a href="https://www.linkedin.com/in/anujmaha/">@anujmaha</a></h6>',
unsafe_allow_html=True,
)
st.markdown(
'📧 : anujsmahajan1998@gmail.com',
)
st.markdown(
'<div style="margin-top: 0.75em;"><img src="https://cdn.buymeacoffee.com/buttons/default-orange.png" alt="Buy Me A Coffee" height="41" width="174"></a></div>',
unsafe_allow_html=True,
)
if "user_prompt_history" not in st.session_state:
st.session_state["user_prompt_history"] = []
if "chat_answers_history" not in st.session_state:
st.session_state["chat_answers_history"] = []
if "chat_history" not in st.session_state:
st.session_state["chat_history"] = []
def create_sources_string(source_urls: Set[str]) -> str:
if not source_urls:
return ""
sources_list = list(source_urls)
sources_list.sort()
sources_string = "sources:\n"
for i, source in enumerate(sources_list):
sources_string += f"{i+1}. {source}\n"
return sources_string
prompt = st.text_input("Prompt", placeholder="Enter your Langchain related query here... (eg, what is ConversationalRetrievalChain ?)")
if prompt:
with st.spinner("Generating response..."):
generated_response = run_llm(
query=prompt, chat_history=st.session_state["chat_history"]
)
sources = set(
[doc.metadata["source"] for doc in generated_response["source_documents"]]
)
formatted_response = (
f"{generated_response['answer']} \n\n {create_sources_string(sources)}"
)
st.session_state.user_prompt_history.append(prompt)
st.session_state.chat_answers_history.append(formatted_response)
st.session_state.chat_history.append((prompt, generated_response["answer"]))
if st.session_state["chat_answers_history"]:
for generated_response, user_query in zip(
st.session_state["chat_answers_history"],
st.session_state["user_prompt_history"],
):
message(
user_query,
is_user=True,
avatar_style="adventurer",
seed=123,
)
# message(generated_response)
st.write(
f'<div style="word-wrap: break-word;">{generated_response}</div>',
unsafe_allow_html=True,
)
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