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import streamlit as st
import re
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='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 ---
PAGE_KEY = "machine_learning_chat_history"
try:
# --- Session State ---
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:
# Add system context
system_prompt = f"""Act as a Machine learning mentor with {experience_label} years of experience. Teach in a friendly, approachable manner while following these strict rules:
1. Only answer questions related to machine learning programming (including libraries, frameworks, and tools in the machine learning ecosystem)
2. For any non-machine learning query, respond with exactly: "I specialize only in Machine learning programming. This appears to be a non-machine learning topic."
3. Never suggest you can help with non-machine learning 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 machine learning 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 ---
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("---")
except:
st.warning('The token limit has reached please revisit in 24 hours!') |