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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)}")
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