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import streamlit as st
import os
from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
from langchain_core.messages import HumanMessage, SystemMessage
# Set Hugging Face tokens
hf = os.getenv('HF_TOKEN')
os.environ['HUGGINGFACEHUB_API_TOKEN'] = hf
os.environ['HF_TOKEN'] = hf
# --- Page Configuration ---
st.set_page_config(page_title="Machine Learning Mentor", layout="wide")
# --- Custom CSS ---
# st.markdown("""
# <style>
# body {
# font-family: 'Segoe UI', sans-serif;
# background: linear-gradient(135deg, #2c003e, #0f9b8e);
# }
# .main {
# padding: 2rem;
# }
# .stTextInput>div>div>input {
# color: #ffffff !important;
# background-color: #00000010;
# border: 1px solid #ffffff30;
# }
# .chat-bubble {
# padding: 1rem;
# margin: 0.5rem 0;
# border-radius: 12px;
# max-width: 80%;
# }
# .user-bubble {
# background-color: #4b0082;
# color: white;
# margin-left: auto;
# text-align: right;
# }
# .mentor-bubble {
# background-color: #00bfa6;
# color: white;
# margin-right: auto;
# }
# .stButton>button {
# background: #ffffff20;
# border: 1px solid #ffffff50;
# color: white;
# font-weight: bold;
# border-radius: 10px;
# transition: 0.3s ease;
# }
# .stButton>button:hover {
# background: #ffffff40;
# }
# </style>
# """, unsafe_allow_html=True)
# 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)
# --- Title ---
st.title("🤖 Machine Learning Mentor")
# --- Sidebar Preferences ---
st.sidebar.title("Mentor Preferences")
experience_label = st.sidebar.selectbox("Select your experience level:", ["Beginner", "Intermediate", "Experienced"])
# --- Initialize Model ---
ml_model_skeleton = HuggingFaceEndpoint(
repo_id='Qwen/Qwen3-14B',
provider='nebius',
temperature=0.7,
max_new_tokens=50,
task='conversational'
)
ml_mentor = ChatHuggingFace(
llm=ml_model_skeleton,
repo_id='Qwen/Qwen3-14B',
provider='nebius',
temperature=0.7,
max_new_tokens=50,
task='conversational'
)
PAGE_KEY = "ml_chat_history"
if PAGE_KEY not in st.session_state:
st.session_state[PAGE_KEY] = []
# --- Layout ---
col1, col2 = st.columns([3, 1])
# --- Chat Section ---
with col1:
with st.form(key="chat_form"):
user_input = st.text_input("Ask your question:")
submit = st.form_submit_button("Send")
if submit and user_input:
system_prompt = f"""You are a seasoned Machine Learning mentor with {experience_label} years of hands-on expertise. Your teaching style is friendly, clear, and approachable. Follow these strict guidelines:
1. Only respond to questions directly related to machine learning programming — including its libraries, tools, and frameworks.
2. If asked about anything outside machine learning, reply with: "I specialize only in Machine learning programming. This appears to be a non-machine learning topic."
3. Do not offer help on topics unrelated to machine learning.
4. Keep your explanations beginner-friendly when needed, focusing on clarity and real-world application.
5. Use practical code examples and scenarios to reinforce learning.
6. For complex or advanced topics, assume the learner has foundational knowledge of machine learning concepts."""
messages = [SystemMessage(content=system_prompt), HumanMessage(content=user_input)]
result = ml_mentor.invoke(messages)
st.session_state[PAGE_KEY].append((user_input, result.content))
st.subheader("🗨️ Chat History")
# for user, bot in st.session_state[PAGE_KEY]:
# st.markdown(f'<div class="chat-bubble user-bubble">{user}</div>', unsafe_allow_html=True)
# st.markdown(f'<div class="chat-bubble mentor-bubble">{bot}</div>', unsafe_allow_html=True)
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)
# --- Mentor Tips Sidebar ---
# with col2:
# st.markdown("### 💡 Tips from Mentor")
# st.info("Try asking about:\n- Regression vs Classification\n- Overfitting examples\n- Feature scaling\n- Model evaluation techniques")
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