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

# Set environment variables for Hugging Face token
hf = os.getenv('Data_science')
os.environ['HUGGINGFACEHUB_API_TOKEN'] = hf
os.environ['HF_TOKEN'] = hf

# Page config
st.set_page_config(page_title="Deep Learning Mentor Chat", layout="centered")

# Inject CSS styling from homepage
st.markdown("""
    <style>
    .main {
        background: linear-gradient(135deg, #430089 0%, #82ffa1 100%);
        padding: 2rem;
        font-family: 'Segoe UI', sans-serif;
    }
    .stButton>button {
        background: #ffffff10;
        border: 2px solid #ffffff50;
        color: white;
        font-size: 18px;
        font-weight: 600;
        padding: 0.8em 1.2em;
        border-radius: 12px;
        width: 100%;
        transition: 0.3s ease;
        box-shadow: 0 4px 10px rgba(0, 0, 0, 0.15);
    }
    .stButton>button:hover {
        background: #ffffff30;
        border-color: #fff;
        color: #ffffff;
    }
    h1, h3, p, label {
        color: #ffffff;
        text-align: center;
    }
    hr {
        border: 1px solid #ffffff50;
        margin: 2em 0;
    }
    .css-1aumxhk {
        color: white;
    }
    </style>
""", unsafe_allow_html=True)

# Title
st.title("🧠 Deep Learning Mentor Chat")

# Sidebar experience selector
st.sidebar.title("Mentor Preferences")
exp = st.sidebar.selectbox("Select experience level:", ['Beginner', 'Intermediate', 'Expert'])

# Initialize LLM
mentor_llm = HuggingFaceEndpoint(
    repo_id='Qwen/Qwen3-32B',
    provider='sambanova',
    temperature=0.7,
    max_new_tokens=150,
    task='conversational'
)

deep_mentor = ChatHuggingFace(
    llm=mentor_llm,
    repo_id='Qwen/Qwen3-32B',
    provider='sambanova',
    temperature=0.7,
    max_new_tokens=150,
    task='conversational'
)

# Session key
PAGE_KEY = "deep_learning_chat_history"
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")

# Handle submission
if submit and user_input:
    system_prompt = (
        f"You are a deep learning mentor with {exp.lower()} level expertise. "
        f"Answer only deep learning-related questions, teach in a friendly tone, and limit responses to 150 words. "
        f"If a question is outside deep learning, politely say it's out of scope."
    )
    messages = [SystemMessage(content=system_prompt), HumanMessage(content=user_input)]
    result = deep_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("---")