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import streamlit as st | |
import requests | |
# ----------------------------------- | |
# 1. Hugging Face API Configuration | |
# ----------------------------------- | |
API_URL = "https://api-inference.huggingface.co/models/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B" | |
def query(payload): | |
""" | |
Query the Hugging Face Inference API with the given payload. | |
Keeps the original approach: payload = {"inputs": user_input}. | |
""" | |
headers = {"Authorization": f"Bearer {st.secrets['HF_TOKEN']}"} | |
response = requests.post(API_URL, headers=headers, json=payload) | |
return response.json() | |
# ----------------------------------- | |
# 2. Streamlit Page Settings | |
# ----------------------------------- | |
st.set_page_config( | |
page_title="DeepSeek Chatbot - ruslanmv.com", | |
page_icon="🤖", | |
layout="centered" | |
) | |
# ----------------------------------- | |
# 3. Session State Initialization | |
# ----------------------------------- | |
# We'll keep a chat history in st.session_state | |
if "messages" not in st.session_state: | |
st.session_state.messages = [] | |
# ----------------------------------- | |
# 4. Sidebar Configuration | |
# ----------------------------------- | |
with st.sidebar: | |
st.header("Configuration") | |
st.markdown("[Get your HuggingFace Token](https://huggingface.co/settings/tokens)") | |
# Although these parameters are shown on the sidebar, we won't actually | |
# pass them to the payload in `query()`, to strictly preserve the "original" approach. | |
st.write("**NOTE:** These sliders do not affect the inference in this demo.") | |
system_message = st.text_area( | |
"System Message (display only)", | |
value="You are a friendly Chatbot created by ruslanmv.com", | |
height=100 | |
) | |
max_tokens = st.slider("Max Tokens (not used here)", 1, 4000, 512) | |
temperature = st.slider("Temperature (not used here)", 0.1, 4.0, 0.7) | |
top_p = st.slider("Top-p (not used here)", 0.1, 1.0, 0.9) | |
# ----------------------------------- | |
# 5. Main Chat Interface | |
# ----------------------------------- | |
st.title("🤖 DeepSeek Chatbot") | |
st.caption("Powered by Hugging Face Inference API - Original Inference Approach") | |
# Display the chat history, message by message | |
for message in st.session_state.messages: | |
with st.chat_message(message["role"]): | |
st.markdown(message["content"]) | |
# ----------------------------------- | |
# 6. Capture User Input | |
# ----------------------------------- | |
if user_input := st.chat_input("Type your message..."): | |
# 6.1 Append user message to chat history | |
st.session_state.messages.append({"role": "user", "content": user_input}) | |
# Display user's message | |
with st.chat_message("user"): | |
st.markdown(user_input) | |
# ----------------------------------- | |
# 7. Query the Model | |
# ----------------------------------- | |
try: | |
with st.spinner("Generating response..."): | |
# Prepare payload with the original approach | |
payload = {"inputs": user_input} | |
output = query(payload) | |
# Check if the output is valid | |
if ( | |
isinstance(output, list) | |
and len(output) > 0 | |
and "generated_text" in output[0] | |
): | |
assistant_response = output[0]["generated_text"] | |
else: | |
assistant_response = ( | |
"Error: Unable to generate a response. Please try again." | |
) | |
# Display the assistant's response | |
with st.chat_message("assistant"): | |
st.markdown(assistant_response) | |
# Store assistant's response in chat history | |
st.session_state.messages.append( | |
{"role": "assistant", "content": assistant_response} | |
) | |
except Exception as e: | |
st.error(f"Application Error: {str(e)}") | |