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import streamlit as st
import requests
import logging
import os
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
st.set_page_config(
page_title="DeepSeek Chatbot",
page_icon="🤖",
layout="centered"
)
if "messages" not in st.session_state:
st.session_state.messages = []
with st.sidebar:
st.header("Model Configuration")
# st.markdown("[Get HuggingFace Token](https://huggingface.co/settings/tokens)")
model_options = [
"deepseek-ai/DeepSeek-R1-Distill-Qwen-32B",
# "deepseek-ai/DeepSeek-R1-Distill-Qwen-14B",
# "deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
# "deepseek-ai/DeepSeek-R1-Distill-Llama-70B",
# "deepseek-ai/DeepSeek-R1-Distill-Llama-8B"
]
selected_model = st.selectbox("Select Model", model_options, index=0)
system_message = st.text_area(
"System Message",
value=(
"You are an intelligent and helpful AI assistant. "
"Your goal is to provide clear, concise, and informative responses to user queries. "
"Be polite, professional, and friendly while ensuring accuracy. "
"If a user asks an unclear question, kindly ask for clarification. "
"When providing information, make sure it is easy to understand. "
"Keep your tone engaging and supportive, adapting to the user's needs."
),
height=150
)
max_tokens = st.slider(
"Max Tokens",
1, 4000, 512
)
temperature = st.slider(
"Temperature",
0.1, 4.0, 0.7
)
top_p = st.slider(
"Top-p",
0.1, 1.0, 0.9
)
def query(payload, api_url):
HF_TOKEN = os.getenv("HF_TOKEN")
if not HF_TOKEN:
st.error("Missing Hugging Face API token. Please set 'HF_TOKEN' in the Hugging Face Spaces secrets.")
headers = {"Authorization": f"Bearer {HF_TOKEN}"}
logger.info(f"Sending request to {api_url} with payload: {payload}")
response = requests.post(api_url, headers=headers, json=payload)
logger.info(f"Received response: {response.status_code}, {response.text}")
return response.json()
st.title("🤖 DeepSeek Chatbot")
st.caption("Powered by Hugging Face Inference API - Configure in sidebar")
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
if prompt := st.chat_input("Type your message..."):
st.session_state.messages.append({"role": "user", "content": prompt})
with st.chat_message("user"):
st.markdown(prompt)
try:
with st.spinner("Generating response..."):
# Prepare the payload for the API
payload = {
"inputs": prompt,
"parameters": {
"max_new_tokens": max_tokens,
"temperature": temperature,
"top_p": top_p,
"return_full_text": False
}
}
# Dynamically construct the API URL based on the selected model
api_url = f"https://api-inference.huggingface.co/models/{selected_model}"
logger.info(f"Selected model: {selected_model}, API URL: {api_url}")
# Query the Hugging Face API using the selected model
output = query(payload, api_url)
# Handle API response
if isinstance(output, list) and len(output) > 0 and 'generated_text' in output[0]:
assistant_response = output[0]['generated_text']
logger.info(f"Generated response: {assistant_response}")
with st.chat_message("assistant"):
st.markdown(assistant_response)
st.session_state.messages.append({"role": "assistant", "content": assistant_response})
else:
logger.error(f"Unexpected API response: {output}")
st.error("Error: Unable to generate a response. Please try again.")
except Exception as e:
logger.error(f"Application Error: {str(e)}", exc_info=True)
st.error(f"Application Error: {str(e)}")
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