import streamlit as st
#Config
st.set_page_config(layout="wide", page_icon="💬", page_title="Robby | Chat-Bot 🤖")
#Contact
with st.sidebar.expander("📬 Contact"):
st.write("**GitHub:**",
"[yvann-hub/Robby-chatbot](https://github.com/yvann-hub/Robby-chatbot)")
st.write("**Medium:** "
"[@yvann-hub](https://medium.com/@yvann-hub)")
st.write("**Twitter:** [@yvann_hub](https://twitter.com/yvann_hub)")
st.write("**Mail** : barbot.yvann@gmail.com")
st.write("**Created by Yvann**")
#Title
st.markdown(
"""
Robby, your data-aware assistant 🤖
""",
unsafe_allow_html=True,)
st.markdown("---")
#Description
st.markdown(
"""
I'm Robby, an intelligent chatbot created by combining
the strengths of Langchain and Streamlit. I use large language models to provide
context-sensitive interactions. My goal is to help you better understand your data.
I support PDF, TXT, CSV, Youtube transcript ðŸ§
""",
unsafe_allow_html=True)
st.markdown("---")
#Robby's Pages
st.subheader("🚀 Robby's Pages")
st.write("""
- **Robby-Chat**: General Chat on data (PDF, TXT,CSV) with a [vectorstore](https://github.com/facebookresearch/faiss) (index useful parts(max 4) for respond to the user) | works with [ConversationalRetrievalChain](https://python.langchain.com/en/latest/modules/chains/index_examples/chat_vector_db.html)
- **Robby-Sheet** (beta): Chat on tabular data (CSV) | for precise information | process the whole file | works with [CSV_Agent](https://python.langchain.com/en/latest/modules/agents/toolkits/examples/csv.html) + [PandasAI](https://github.com/gventuri/pandas-ai) for data manipulation and graph creation
- **Robby-Youtube**: Summarize YouTube videos with [summarize-chain](https://python.langchain.com/en/latest/modules/chains/index_examples/summarize.html)
""")
st.markdown("---")
#Contributing
st.markdown("### 🎯 Contributing")
st.markdown("""
**Robby is under regular development. Feel free to contribute and help me make it even more data-aware!**
""", unsafe_allow_html=True)