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| import streamlit as st | |
| import os | |
| import torch | |
| torch.classes.__path__ = [] | |
| os.environ["STREAMLIT_FILE_WATCHER_TYPE"]="none" | |
| from chatbot_generate import generate_response | |
| st.set_page_config(page_title="TinyLLaMA Chat", page_icon="🦙") | |
| st.title("🦙 Customer ChatBot") | |
| # Initialize session state | |
| if "chat_history" not in st.session_state: | |
| st.session_state.chat_history = [] | |
| if "last_user_index" not in st.session_state: | |
| st.session_state.last_user_index = -1 | |
| # Sidebar with hint buttons stacked vertically | |
| st.sidebar.markdown("### 💡 Hint Buttons") | |
| hints = [ | |
| "Can I buy fractional shares of ultra-expensive NFTs that cost millions of dollars?", | |
| "When I sell my NFT on Exclusible, how much goes to the original artist as royalties?", | |
| "I have NFTs on Solana and Polygon blockchains. Can I display these in my Exclusible metaverse gallery or do you only support Ethereum NFTs?" | |
| ] | |
| for hint in hints: | |
| if st.sidebar.button(hint): | |
| st.session_state.chat_history.append({"role": "user", "content": hint}) | |
| # Main chat area | |
| for msg in st.session_state.chat_history: | |
| with st.chat_message(msg["role"]): | |
| st.markdown(msg["content"]) | |
| def process_new_user_message(): | |
| last_user_msg_indices = [i for i, m in enumerate(st.session_state.chat_history) if m["role"] == "user"] | |
| if not last_user_msg_indices: | |
| return | |
| last_index = last_user_msg_indices[-1] | |
| if last_index > st.session_state.last_user_index: | |
| user_msg = st.session_state.chat_history[last_index]["content"] | |
| full_output="" | |
| with st.chat_message("assistant"): | |
| with st.spinner("Thinking..."): | |
| output=st.write_stream(generate_response(user_msg)) | |
| st.session_state.chat_history.append({"role": "assistant", "content": output}) | |
| st.session_state.last_user_index = last_index | |
| process_new_user_message() | |
| if prompt := st.chat_input("Type your message here..."): | |
| st.session_state.chat_history.append({"role": "user", "content": prompt}) | |
| with st.chat_message("user"): | |
| st.markdown(prompt) | |
| process_new_user_message() | |