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(""" # # """, unsafe_allow_html=True) # Improved custom CSS st.markdown(""" """, 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'
{user}
', unsafe_allow_html=True) # st.markdown(f'
{bot}
', unsafe_allow_html=True) 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) # --- 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")