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| import requests | |
| import streamlit as st | |
| from layouts.mainlayout import mainlayout | |
| from langchain.memory.chat_message_histories import StreamlitChatMessageHistory | |
| from components.file_streaming import * | |
| def display(): | |
| with st.expander("What happens when I upload a PDF? 📑", expanded=True): | |
| st.info( | |
| """ | |
| - The PDF is uploaded to the backend server. ⚙️ | |
| - The PDF is converted into small chunks for faster processing. 🚀 | |
| - The chunks are broken down into tokens. A token is a single word or a group of words. 📝 | |
| - The tokens are converted into embedding vectors. 📊 | |
| - The embedding vectors are stored in a vector store. 🗄️ | |
| """, | |
| icon="ℹ️", | |
| ) | |
| st.divider() | |
| display() | |
| uploaded_files = st.sidebar.file_uploader(label="Upload PDF files", type=["pdf"]) | |
| if not uploaded_files: | |
| st.info("Please upload PDF documents to continue.") | |
| st.stop() | |
| upload_data(uploaded_files) | |
| msgs = StreamlitChatMessageHistory() | |
| if len(msgs.messages) == 0 or st.sidebar.button("Clear message history"): | |
| msgs.clear() | |
| msgs.add_ai_message("How can I help you?") | |
| avatars = {"human": "user", "ai": "assistant"} | |
| for msg in msgs.messages: | |
| st.chat_message(avatars[msg.type]).write(msg.content) | |
| if user_query := st.chat_input(placeholder="Ask me anything!"): | |
| st.chat_message("user").write(user_query) | |
| with st.chat_message("assistant"): | |
| retrieval_handler = PrintRetrievalHandler(st.container()) | |
| stream_handler = StreamHandler(st.empty()) | |
| response = requests.post( | |
| "http://127.0.0.1:8000/api/inference", | |
| json={"promptMessage": user_query}, | |
| ).json() | |