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import streamlit as st
import requests
import logging
import json
# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# Page configuration
st.set_page_config(
page_title="DeepSeek Chatbot",
page_icon="🤖",
layout="centered"
)
# Initialize session state for chat history
if "messages" not in st.session_state:
st.session_state.messages = []
# Sidebar configuration
with st.sidebar:
st.header("Model Configuration")
st.markdown("[Get HuggingFace Token](https://huggingface.co./settings/tokens)")
# Dropdown to select model
model_options = [
"deepseek-ai/DeepSeek-R1-Distill-Qwen-32B",
]
selected_model = st.selectbox("Select Model", model_options, index=0)
system_message = st.text_area(
"System Message",
value="You are a deep thinking AI, you may use extremely long chains of thought to deeply consider the problem and deliberate with yourself via systematic reasoning processes to help come to a correct solution prior to answering. You should enclose your thoughts and internal monologue inside tags, and then provide your solution or response to the problem.",
height=100
)
max_tokens = st.slider(
"Max Tokens",
10, 4000, 1000
)
temperature = st.slider(
"Temperature",
0.1, 4.0, 0.3
)
top_p = st.slider(
"Top-p",
0.1, 1.0, 0.6
)
# Function to query the Hugging Face API
def query(payload, api_url):
headers = {"Authorization": f"Bearer {st.secrets['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}")
try:
return response.json()
except requests.exceptions.JSONDecodeError:
logger.error(f"Failed to decode JSON response: {response.text}")
return None
# Function to perform a web search using SerpHouse API
def search_web(query):
search_url = f"https://api.serphouse.com/serp/live?api_token=V38CNn4HXpLtynJQyOeoUensTEYoFy8PBUxKpDqAW1pawT1vfJ2BWtPQ98h6&q={query}&responseType=json&domain=google&lang=en&serp_type=web"
response = requests.get(search_url)
if response.status_code == 200:
return response.json()
else:
logger.error(f"Error while searching: {response.status_code}")
return None
# Chat interface
st.title("🤖 DeepSeek Chatbot")
st.caption("Powered by Hugging Face Inference API - Configure in sidebar")
# Display chat history
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
# Handle input
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..."):
# Perform a web search based on the user input
search_results = search_web(prompt)
# Process search results if available
if search_results and "results" in search_results:
search_content = "\n".join([f"**{item['title']}**: {item['snippet']}" for item in search_results["results"]["organic"]])
search_content = f"Here are some search results related to your question:\n\n{search_content}\n\n"
# Combine the system message, search results, and user input into a single prompt
full_prompt = f"{system_message}\n\n{search_content}User: {prompt}\nAssistant:"
else:
full_prompt = f"{system_message}\n\nUser: {prompt}\nAssistant:"
payload = {
"inputs": full_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 output is not None and isinstance(output, list) and len(output) > 0:
if 'generated_text' in output[0]:
# Extract the assistant's response
assistant_response = output[0]['generated_text'].strip()
# Check for and remove duplicate responses
responses = assistant_response.split("\n</think>\n")
unique_response = responses[0].strip()
logger.info(f"Generated response: {unique_response}")
# Append response to chat only once
with st.chat_message("assistant"):
st.markdown(unique_response)
st.session_state.messages.append({"role": "assistant", "content": unique_response})
else:
logger.error(f"Unexpected API response structure: {output}")
st.error("Error: Unexpected response from the model. Please try again.")
else:
logger.error(f"Empty or invalid API response: {output}")
st.error("Error: Unable to generate a response. Please check the model and 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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