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import streamlit as st
import requests
import os
# Initialize chat history in session state
if 'chat_history' not in st.session_state:
st.session_state.chat_history = []
st.title("DeepSeek-V3 Chatbot")
# Display chat messages from history
for message in st.session_state.chat_history:
st.write(f"**{message['role'].capitalize()}:** {message['content']}")
# Input for user message
user_input = st.text_input("You:", key="user_input")
if st.button("Send"):
if user_input:
# Append user message to chat history
st.session_state.chat_history.append({"role": "user", "content": user_input})
# Prepare payload for Hugging Face Inference API
payload = {
"inputs": {
"past_user_inputs": [msg['content'] for msg in st.session_state.chat_history if msg['role'] == 'user'],
"generated_responses": [msg['content'] for msg in st.session_state.chat_history if msg['role'] == 'assistant'],
"text": user_input
}
}
# Fetch API token from environment variables
API_TOKEN = os.getenv("API_TOKEN")
if not API_TOKEN:
st.error("API_TOKEN not found. Please set it as an environment variable.")
else:
api_url = "https://api-inference.huggingface.co/models/deepseek-ai/DeepSeek-V3"
headers = {"Authorization": f"Bearer {API_TOKEN}"}
# Send request to Hugging Face Inference API
response = requests.post(api_url, headers=headers, json=payload)
if response.status_code == 200:
assistant_reply = response.json().get('generated_text', '')
# Append assistant's reply to chat history
st.session_state.chat_history.append({"role": "assistant", "content": assistant_reply})
st.write(f"**Assistant:** {assistant_reply}")
else:
st.error("Error: Unable to get response from DeepSeek-V3 model.")
else:
st.warning("Please enter a message.")
if st.button("Clear Chat"):
st.session_state.chat_history = [] |