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import streamlit as st
import os
from langchain_community.chat_models import ChatHuggingFace
from langchain_community.llms import HuggingFaceEndpoint
from langchain_core.messages import HumanMessage, SystemMessage, AIMessage
hf_token = os.getenv("Data_science")
os.environ["HUGGINGFACEHUB_API_KEY"] = hf_token
os.environ["HF_TOKEN"] = hf_token
model = HuggingFaceEndpoint(
repo_id="Qwen/Qwen3-32B",
provider="nebius",
temperature=0.6,
max_new_tokens=300,
task="conversational"
)
chat_model = ChatHuggingFace(
llm=model,
repo_id="Qwen/Qwen3-32B",
provider="nebius",
temperature=0.6,
max_new_tokens=300,
task="conversational"
)
# --- Streamlit styles ---
st.markdown("""
<style>
.subject-btn {
display: inline-block;
margin: 0.3em;
}
.output-box {
background-color: #f9f9f9;
border-radius: 10px;
padding: 20px;
margin-top: 20px;
box-shadow: 0 2px 6px rgba(0, 0, 0, 0.05);
}
</style>
""", unsafe_allow_html=True)
# --- Session state ---
if "message_history" not in st.session_state:
st.session_state.message_history = []
if "selected_subject" not in st.session_state:
st.session_state.selected_subject = "Python"
# --- UI Header ---
st.title("🎓 Data Science Mentor")
st.markdown("Ask subject-specific questions and get guidance based on your experience level.")
# --- Experience level ---
experience = st.selectbox("👤 Select your experience level:", ["Beginner", "Intermediate", "Expert"])
# --- Subject buttons ---
st.markdown("### 📚 Choose a Subject:")
cols = st.columns(4)
subjects = ["Python", "SQL", "Power BI", "Statistics", "Machine Learning", "Deep Learning", "Generative AI"]
for i, subject in enumerate(subjects):
if cols[i % 4].button(subject):
st.session_state.selected_subject = subject
st.session_state.message_history = [] # Reset chat on subject change
# --- System Message ---
system_prompt = f"""
You are a highly knowledgeable data science mentor specialized in {st.session_state.selected_subject}.
Your job is to guide a {experience.lower()} learner with clear, concise, and actionable advice.
Explain concepts, best practices, and answer questions with patience and professionalism.
If relevant, include example code, use-cases, or tips.
"""
if not st.session_state.message_history:
st.session_state.message_history.append(SystemMessage(content=system_prompt))
# --- Chat Input ---
user_question = st.text_input(f"💬 Ask your {st.session_state.selected_subject} question:")
if st.button("Ask Mentor"):
if user_question.strip():
with st.spinner("Thinking..."):
st.session_state.message_history.append(HumanMessage(content=user_question))
try:
response = chat_model.invoke(st.session_state.message_history)
st.session_state.message_history.append(AIMessage(content=response.content))
st.markdown('<div class="output-box">', unsafe_allow_html=True)
st.markdown("### 🧠 Mentor's Response:")
st.markdown(response.content)
st.markdown("</div>", unsafe_allow_html=True)
except Exception as e:
st.error(f"❌ Error: {e}")
else:
st.warning("⚠️ Please enter a question before submitting.")
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