import streamlit as st import os from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace from langchain_core.messages import HumanMessage, SystemMessage # Load Hugging Face token from environment hf = os.getenv('Data_science') os.environ['HUGGINGFACEHUB_API_TOKEN'] = hf os.environ['HF_TOKEN'] = hf # --- Page Config --- st.set_page_config(page_title="GenAI Mentor Chat", layout="centered") # --- Custom CSS Styling --- st.markdown(""" """, unsafe_allow_html=True) # --- Title --- st.title("🤖 GenAI Mentor Chat") # --- Sidebar for Experience --- st.sidebar.title("Mentor Preferences") experience_label = st.sidebar.selectbox("Select your experience level:", ["Beginner", "Intermediate", "Expert"]) # --- Model Initialization --- genai_skeleton = HuggingFaceEndpoint( repo_id='google/gemma-2-9b-it', provider='nebius', temperature=0.7, max_new_tokens=50, task='conversational' ) genai_chat = ChatHuggingFace( llm=genai_skeleton, repo_id='google/gemma-2-9b-it', provider='nebius', temperature=0.7, max_new_tokens=50, task='conversational' ) PAGE_KEY = "genai_chat_history" # --- Session State Initialization --- 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") # --- Chat Logic --- if submit and user_input: system_prompt = ( f"Act as a Generative AI mentor with {experience_label.lower()} expertise. " f"Explain concepts in a friendly tone, within 150 words. " f"If the question is not related to Generative AI, politely say it's out of scope." ) messages = [SystemMessage(content=system_prompt), HumanMessage(content=user_input)] result = genai_chat.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("---")