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| import streamlit as st | |
| import requests | |
| import logging | |
| # Configure logging | |
| logging.basicConfig(level=logging.INFO) | |
| logger = logging.getLogger(__name__) | |
| # Page configuration | |
| st.set_page_config( | |
| page_title="DeepSeek Chatbot - ruslanmv.com", | |
| 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", | |
| # "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 a friendly Chatbot created by ruslanmv.com", | |
| height=100 | |
| ) | |
| 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 | |
| ) | |
| # 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}") | |
| return response.json() | |
| # 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..."): | |
| # 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)}") | |