import streamlit as st import os from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace from langchain_core.messages import HumanMessage, SystemMessage # Set environment variables hf = os.getenv('HF_TOKEN') os.environ['HUGGINGFACEHUB_API_KEY'] = hf os.environ['HF_TOKEN'] = hf # Initialize DeepSeek model (you can replace this with your own model if needed) def load_deepseek_model(): deep_seek_model = HuggingFaceEndpoint( repo_id="deepseek-ai/DeepSeek-R1", provider="nebius", temperature=0.7, max_new_tokens=150, task="conversational" ) return ChatHuggingFace(llm=deep_seek_model) sql_mentor = load_deepseek_model() # Streamlit page setup st.set_page_config(page_title="SQL Mentor Chat", layout="centered") # Improved custom CSS st.markdown(""" """, unsafe_allow_html=True) # Page title st.markdown("

🗃️ SQL Mentor Chat

", unsafe_allow_html=True) st.markdown("

Learn SQL interactively from your AI mentor!

", unsafe_allow_html=True) # Sidebar for user preference st.sidebar.title("Mentor Preferences") exp = st.sidebar.selectbox("Choose your experience level:", ["Beginner", "Intermediate", "Expert"]) # Session state key PAGE_KEY = "chat_history" # Initialize session state if PAGE_KEY not in st.session_state: st.session_state[PAGE_KEY] = [] # Chat input box st.markdown("
", unsafe_allow_html=True) with st.form(key="chat_form"): user_input = st.text_input("💬 Ask me anything about SQL:") submit = st.form_submit_button("Send") # Chat logic if submit and user_input: system_prompt = ( f"Act as a SQL mentor with {exp.lower()} expertise. " f"Answer in a friendly tone and within 150 words. " f"If the question is not SQL-related, politely say it's out of scope." ) messages = [SystemMessage(content=system_prompt), HumanMessage(content=user_input)] result = sql_mentor.invoke(messages) st.session_state[PAGE_KEY].append((user_input, result.content)) # Show conversation as chat bubbles if st.session_state[PAGE_KEY]: st.markdown('
', unsafe_allow_html=True) for user, bot in st.session_state[PAGE_KEY]: st.markdown(f'
👤 You: {user}
', unsafe_allow_html=True) st.markdown(f'
🧑‍🏫 Mentor: {bot}
', unsafe_allow_html=True) st.markdown('
', unsafe_allow_html=True)