Spaces:
Running
Running
File size: 4,422 Bytes
f50b5cb 0afb4fe f50b5cb 0afb4fe f50b5cb f9b44c9 f50b5cb 0afb4fe f50b5cb f9b44c9 0afb4fe f50b5cb 0afb4fe f9b44c9 f50b5cb f9b44c9 0afb4fe f50b5cb 0afb4fe f50b5cb 0afb4fe f50b5cb 6fd2cd0 93ce395 f50b5cb |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 |
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('HF_TOKEN')
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")
# Improved custom CSS
st.markdown("""
<style>
.main {
background: linear-gradient(to right, #1f4037, #99f2c8);
padding: 2rem;
font-family: 'Segoe UI', sans-serif;
}
.stButton>button {
background-color: #ffffff10;
color: white;
font-weight: 600;
border-radius: 10px;
padding: 0.6rem 1rem;
transition: all 0.3s ease;
border: 1px solid white;
}
.stButton>button:hover {
background-color: #ffffff30;
color: white;
border-color: #fff;
}
h1, h3, p, label {
color: white;
text-align: center;
}
.chat-bubble-user {
background-color: #ffffff25;
padding: 0.75rem 1rem;
border-radius: 1rem;
margin-bottom: 0.5rem;
color: #fff;
font-weight: 500;
text-align: left;
width: fit-content;
max-width: 90%;
align-self: flex-end;
}
.chat-bubble-bot {
background-color: #ffffff15;
padding: 0.75rem 1rem;
border-radius: 1rem;
margin-bottom: 0.5rem;
color: #fff;
text-align: left;
width: fit-content;
max-width: 90%;
align-self: flex-start;
}
.chat-container {
display: flex;
flex-direction: column;
gap: 0.5rem;
margin-top: 2rem;
}
</style>
""", unsafe_allow_html=True)
# App title
st.markdown("<h1>π€ Deep Learning Mentor Chat</h1>", unsafe_allow_html=True)
st.markdown("<p>Learn Deep Learning with personalized AI mentorship</p>", unsafe_allow_html=True)
# Sidebar for experience level
st.sidebar.title("π Select Your Level")
exp = st.sidebar.selectbox("Experience Level", ["Beginner", "Intermediate", "Expert"])
# Load Deep Learning model
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)
# Session key for conversation
PAGE_KEY = "deep_learning_chat_history"
if PAGE_KEY not in st.session_state:
st.session_state[PAGE_KEY] = []
# Chat input form
st.markdown("<hr>", unsafe_allow_html=True)
with st.form(key="chat_form"):
user_input = st.text_input("π¬ Ask your deep learning question:")
submit = st.form_submit_button("Send")
# Handle chat submission
if submit and user_input:
system_prompt = f"""You are a knowledgeable Deep Learning mentor with {exp} years of practical experience. Your communication style is friendly, supportive, and focused. Please adhere to the following strict instructions:
1. Only respond to queries that are specifically about deep learning programming β this includes related libraries, tools, and frameworks.
2. If a question is outside the scope of deep learning, respond exactly with: "I specialize only in deep learning programming. This appears to be a non-deep learning topic."
3. Do not offer help or advice on non-deep learning subjects.
4. Aim for clarity and practical relevance in your explanations, keeping them beginner-friendly when needed.
5. Reinforce learning through relevant code snippets and applied examples.
6. For more advanced discussions, assume the learner has a working knowledge of deep learning fundamentals."""
messages = [SystemMessage(content=system_prompt), HumanMessage(content=user_input)]
result = deep_mentor.invoke(messages)
st.session_state[PAGE_KEY].append((user_input, result.content))
# Chat history display with bubble styling
if st.session_state[PAGE_KEY]:
st.markdown('<div class="chat-container">', unsafe_allow_html=True)
for user, bot in st.session_state[PAGE_KEY]:
st.markdown(f'<div class="chat-user">π€ <strong>You:</strong> {user}</div>', unsafe_allow_html=True)
st.markdown(f'<div class="chat-bot">π§βπ« <strong>Mentor:</strong> {bot}</div>', unsafe_allow_html=True)
st.markdown('</div>', unsafe_allow_html=True)
|