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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('Data_science')
os.environ['HUGGINGFACEHUB_API_TOKEN'] = hf
os.environ['HF_TOKEN'] = hf
# Page setup
st.set_page_config(page_title="SQL Mentor Chat", layout="centered")
# Inject homepage-style CSS
st.markdown("""
<style>
.main {
background: linear-gradient(135deg, #430089 0%, #82ffa1 100%);
padding: 2rem;
font-family: 'Segoe UI', sans-serif;
}
.stButton>button {
background: #ffffff10;
border: 2px solid #ffffff50;
color: white;
font-size: 18px;
font-weight: 600;
padding: 0.8em 1.2em;
border-radius: 12px;
width: 100%;
transition: 0.3s ease;
box-shadow: 0 4px 10px rgba(0, 0, 0, 0.15);
}
.stButton>button:hover {
background: #ffffff30;
border-color: #fff;
color: #ffffff;
}
h1, h3, p, label {
color: #ffffff;
text-align: center;
}
hr {
border: 1px solid #ffffff50;
margin: 2em 0;
}
</style>
""", unsafe_allow_html=True)
# Page title
st.title("🗃️ SQL Mentor Chat")
# Sidebar experience selector
st.sidebar.title("Mentor Preferences")
exp = st.sidebar.selectbox("Select your experience level:", ["Beginner", "Intermediate", "Expert"])
# Initialize model
sql_model_skeleton = HuggingFaceEndpoint(
repo_id='HuggingFaceH4/zephyr-7b-beta',
provider='hf-inference',
temperature=0.7,
max_new_tokens=110,
task='conversational'
)
sql_mentor = ChatHuggingFace(
llm=sql_model_skeleton,
repo_id='HuggingFaceH4/zephyr-7b-beta',
provider='hf-inference',
temperature=0.7,
max_new_tokens=110,
task='conversational'
)
# Session key
PAGE_KEY = "chat_history_sql"
if PAGE_KEY not in st.session_state:
st.session_state[PAGE_KEY] = []
# Chat input
with st.form(key="chat_form"):
user_input = st.text_input("Ask your question:")
submit = st.form_submit_button("Send")
# Handle query
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 history
st.subheader("🗨️ Chat History")
for user, bot in st.session_state[PAGE_KEY]:
st.markdown(f"**You:** {user}")
st.markdown(f"**Mentor:** {bot}")
st.markdown("---")
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