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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("""
<style>
.main {
background: linear-gradient(to right, #1f4037, #99f2c8);
padding: 2rem;
font-family: 'Segoe UI', sans-serif;
}
.stButton>button {
background-color: #ffffff10;
color: white;
font-weight: 600;
border-radius: 10px;
padding: 0.6rem 1rem;
transition: all 0.3s ease;
border: 1px solid white;
}
.stButton>button:hover {
background-color: #ffffff30;
color: white;
border-color: #fff;
}
h1, h3, p, label {
color: white;
text-align: center;
}
.chat-bubble-user {
background-color: #ffffff25;
padding: 0.75rem 1rem;
border-radius: 1rem;
margin-bottom: 0.5rem;
color: #fff;
font-weight: 500;
text-align: left;
width: fit-content;
max-width: 90%;
align-self: flex-end;
}
.chat-bubble-bot {
background-color: #ffffff15;
padding: 0.75rem 1rem;
border-radius: 1rem;
margin-bottom: 0.5rem;
color: #fff;
text-align: left;
width: fit-content;
max-width: 90%;
align-self: flex-start;
}
.chat-container {
display: flex;
flex-direction: column;
gap: 0.5rem;
margin-top: 2rem;
}
</style>
""", unsafe_allow_html=True)
# Page title
st.markdown("<h1 style='margin-bottom: 0;'>🗃️ SQL Mentor Chat</h1>", unsafe_allow_html=True)
st.markdown("<p style='margin-top: 0.2rem;'>Learn SQL interactively from your AI mentor!</p>", 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("<hr>", 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('<div class="chat-container">', unsafe_allow_html=True)
for user, bot in st.session_state[PAGE_KEY]:
st.markdown(f'<div class="chat-bubble-user">👤 <strong>You:</strong> {user}</div>', unsafe_allow_html=True)
st.markdown(f'<div class="chat-bubble-bot">🧑🏫 <strong>Mentor:</strong> {bot}</div>', unsafe_allow_html=True)
st.markdown('</div>', unsafe_allow_html=True)
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