pages/machine_learning.py

import streamlit as st
import os
from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
from langchain_core.messages import HumanMessage, AIMessage, SystemMessage



hf = os.getenv('Data_science')
os.environ['HUGGINGFACEHUB_API_TOKEN'] = hf
os.environ['HF_TOKEN'] = hf
# --- Config ---
st.set_page_config(page_title="AI Mentor Chat", layout="centered")
st.title("🤖 AI Mentor Chat")

# --- Sidebar for selections ---
st.sidebar.title("Mentor Preferences")

exp1 = ['<1', '1', '2', '3', '4', '5', '5+']
exp = st.sidebar.selectbox("Select experience:", exp1)

# Map experience to label
experience_map = {
    '<1': 'New bie mentor',
    '1': '1', '2': '2', '3': '3', '4': '4', '5': '5',
    '5+': 'Professional'
}
experience_label = experience_map[exp]

# --- Initialize Chat Model ---
deep_seek_skeleton = HuggingFaceEndpoint(
    repo_id='Qwen/Qwen3-14B',
    provider='nebius',
    temperature=0.7,
    max_new_tokens=150,
    task='conversational'
)

deep_seek = ChatHuggingFace(
    llm=deep_seek_skeleton,
    repo_id='Qwen/Qwen3-14B',
    provider='nebius',
    temperature=0.7,
    max_new_tokens=150,
    task='conversational'
)

# --- Session State ---
if "chat_history" not in st.session_state:
    st.session_state.chat_history = []

# --- 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:
    # Add system context
    system_prompt = f"Act as a machine learning mentor who has {experience_label} years of experience who teaches in a very friendly manner and also tells everything in within 150 words. If any question is asked other than machine learning tell politely that this is out of the topic question"

    # Create message list
    messages = [SystemMessage(content=system_prompt), HumanMessage(content=user_input)]

    # Get model response
    result = deep_seek.invoke(messages)

    # Append to history
    st.session_state.chat_history.append((user_input, result.content))

# --- Display Chat History ---
st.subheader("🗨️ Chat History")
for i, (user, bot) in enumerate(st.session_state.chat_history):
    st.markdown(f"**You:** {user}")
    st.markdown(f"**Mentor:** {bot}")
    st.markdown("---")