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



hf = os.getenv('hf')
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='meta-llama/Llama-3.2-3B-Instruct',
    provider='sambanova',
    temperature=0.7,
    max_new_tokens=150,
    task='conversational'
)

deep_seek = ChatHuggingFace(
    llm=deep_seek_skeleton,
    repo_id='meta-llama/Llama-3.2-3B-Instruct',
    provider='sambanova',
    temperature=0.7,
    max_new_tokens=150,
    task='conversational'
)

# --- Session State ---
PAGE_KEY = "python_chat_history" 
try:
    # --- Session State ---
    if PAGE_KEY not in st.session_state:
        st.session_state[PAGE_KEY] = []
    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("---")
    # --- 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 Python mentor with {experience_label} years of experience. Teach in a friendly, approachable manner while following these strict rules:
        1. Only answer questions related to Python programming (including libraries, frameworks, and tools in the Python ecosystem)
        2. For any non-Python query, respond with exactly: "I specialize only in Python programming. This appears to be a non-Python topic."
        3. Never suggest you can help with non-Python 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 Python knowledge"""    
        # 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[PAGE_KEY].append((user_input, result.content))
    
    # --- Display Chat History ---
    
except:
    st.warning('The token limit has reached please revisit in 24 hours!')
































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

# deep_seek_skeleton = HuggingFaceEndpoint(repo_id='meta-llama/Llama-3.2-3B-Instruct',
#                     provider = 'sambanova',
#                     temperature=0.7,
#                     max_new_tokens=150,
#                     task = 'conversational')
# deep_seek = ChatHuggingFace(llm=deep_seek_skeleton,
#                     repo_id='meta-llama/Llama-3.2-3B-Instruct',
#                     provider = 'sambanova',
#                     temperature=0.7,
#                     max_new_tokens=150,
#                     task = 'conversational')

# exp1 = ['<1', '1', '2', '3', '4', '5', '5+']
# exp = st.selectbox("Select experience:", exp1)
# if exp == '<1':
#     experince = 'New bie mentor'
# elif exp == '1':
#     experince = '1'
# elif exp == '2':
#     experince = '2'
# elif exp == '3':
#     experince = '3'
# elif exp == '4':
#     experince = '4'
# elif exp == '5':
#     experince = '5'
# elif exp == '5+':
#     experince = 'professional'




# selec = ['Python', 'Machine Learning', 'Deep Learning', 'Statistics', 'SQL', 'Excel']
# sub = st.selectbox("Select experience:", selec)





    
# user_input = st.text_input("Enter your query:")
# l = []
# st.write(l)
# message = [SystemMessage(content=f'Act as {sub} mentor who has {experince} years of experience and the one who teaches in very friendly manner and also he explains everything within 150 words'),
#                    HumanMessage(content=user_input)]
# while user_input!='end':
#     l.append(user_input)
#     l.append(result.content)
#     st.write(l)
#     user_input = st.text_input("Enter your query:")
#     message = [SystemMessage(content=f'Act as {sub} mentor who has {experince} years of experience and the one who teaches in very friendly manner and also he explains everything within 150 words'),
#                    HumanMessage(content=user_input)]
#     result = deep_seek.invoke(message)