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 🧠
""", 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)