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_TOKEN'] = hf os.environ['HF_TOKEN'] = hf # Page setup st.set_page_config(page_title="Statistics Mentor Chat", layout="centered") # Improved custom CSS st.markdown(""" """, unsafe_allow_html=True) # Title st.title("📊 Statistics Mentor Chat") # Sidebar st.sidebar.title("Mentor Preferences") exp = st.sidebar.selectbox("Select your experience level:", ["Beginner", "Intermediate", "Expert"]) # Model setup stats_model_skeleton = HuggingFaceEndpoint( repo_id='THUDM/GLM-4-32B-0414', provider='novita', temperature=0.7, max_new_tokens=110, task='conversational' ) stats_mentor = ChatHuggingFace( llm=stats_model_skeleton, repo_id='THUDM/GLM-4-32B-0414', provider='novita', temperature=0.7, max_new_tokens=110, task='conversational' ) # Session key PAGE_KEY = "chat_history_stats" if PAGE_KEY not in st.session_state: st.session_state[PAGE_KEY] = [] # Chat form with st.form(key="chat_form"): user_input = st.text_input("Ask your question:") submit = st.form_submit_button("Send") # Chat logic if submit and user_input: system_prompt = ( f"""Act as a statistics mentor with {exp} years of experience. Teach in a friendly, approachable manner while following these strict rules: 1. Only answer questions related to statistics 2. For any non-statistics query, respond with exactly: "I specialize only in statistics programming. This appears to be a non-statistics topic." 3. Never suggest you can help with non-statistics topics 4. Keep explanations clear, practical, and beginner-friendly when appropriate 5. Include practical examples when explaining concepts 6. For advanced topics, assume the student has basic statistics knowledge""" ) messages = [SystemMessage(content=system_prompt), HumanMessage(content=user_input)] result = stats_mentor.invoke(messages) st.session_state[PAGE_KEY].append((user_input, result.content)) # Display chat 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("---")