wehub-resource-sync
155 lines
5.0 KiB
Python
155 lines
5.0 KiB
Python
# Adapted from https://docs.streamlit.io/knowledge-base/tutorials/build-conversational-apps#build-a-simple-chatbot-gui-with-streaming
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import os
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import base64
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import gc
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import random
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import tempfile
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import time
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import uuid
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from IPython.display import Markdown, display
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import streamlit as st
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import torch
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import time
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import numpy as np
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from tqdm import tqdm
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from pdf2image import convert_from_path
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from rag_code import EmbedData, QdrantVDB_QB, Retriever, RAG
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collection_name = "multimodal_rag_with_deepseek-new"
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if "id" not in st.session_state:
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st.session_state.id = uuid.uuid4()
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st.session_state.file_cache = {}
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session_id = st.session_state.id
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def reset_chat():
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st.session_state.messages = []
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st.session_state.context = None
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gc.collect()
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def display_pdf(file):
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# Opening file from file path
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st.markdown("### PDF Preview")
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base64_pdf = base64.b64encode(file.read()).decode("utf-8")
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# Embedding PDF in HTML
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pdf_display = f"""<iframe src="data:application/pdf;base64,{base64_pdf}" width="400" height="100%" type="application/pdf"
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style="height:100vh; width:100%"
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>
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</iframe>"""
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# Displaying File
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st.markdown(pdf_display, unsafe_allow_html=True)
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with st.sidebar:
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st.header(f"Add your documents!")
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uploaded_file = st.file_uploader("Choose your `.pdf` file", type="pdf")
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if uploaded_file:
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try:
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with tempfile.TemporaryDirectory() as temp_dir:
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file_path = os.path.join(temp_dir, uploaded_file.name)
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with open(file_path, "wb") as f:
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f.write(uploaded_file.getvalue())
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file_key = f"{session_id}-{uploaded_file.name}"
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st.write("Indexing your document...")
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if file_key not in st.session_state.get('file_cache', {}):
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# Store Pdf with convert_from_path function
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images = convert_from_path(file_path)
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for i in range(len(images)):
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# Save pages as images in the pdf
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images[i].save('./images/page'+ str(i) +'.jpg', 'JPEG')
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# embed data
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embeddata = EmbedData()
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embeddata.embed(images)
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# set up vector database
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qdrant_vdb = QdrantVDB_QB(collection_name=collection_name,
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vector_dim=128)
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qdrant_vdb.define_client()
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qdrant_vdb.create_collection()
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qdrant_vdb.ingest_data(embeddata=embeddata)
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# set up retriever
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retriever = Retriever(vector_db=qdrant_vdb, embeddata=embeddata)
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# set up rag
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query_engine = RAG(retriever=retriever)
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st.session_state.file_cache[file_key] = query_engine
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else:
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query_engine = st.session_state.file_cache[file_key]
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# Inform the user that the file is processed and Display the PDF uploaded
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st.success("Ready to Chat!")
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display_pdf(uploaded_file)
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except Exception as e:
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st.error(f"An error occurred: {e}")
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st.stop()
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col1, col2 = st.columns([6, 1])
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with col1:
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# st.header("""
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# # Agentic RAG powered by <img src="data:image/png;base64,{}" width="170" style="vertical-align: -3px;">
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# """.format(base64.b64encode(open("assets/deep-seek.png", "rb").read()).decode()))
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st.markdown("""
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# Multimodal RAG powered by <img src="data:image/png;base64,{}" width="170" style="vertical-align: -3px;"> Janus""".format(base64.b64encode(open("assets/deep-seek.png", "rb").read()).decode()), unsafe_allow_html=True)
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with col2:
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st.button("Clear ↺", on_click=reset_chat)
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# Initialize chat history
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if "messages" not in st.session_state:
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reset_chat()
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# Display chat messages from history on app rerun
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for message in st.session_state.messages:
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with st.chat_message(message["role"]):
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st.markdown(message["content"])
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# Accept user input
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if prompt := st.chat_input("What's up?"):
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# Add user message to chat history
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st.session_state.messages.append({"role": "user", "content": prompt})
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# Display user message in chat message container
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with st.chat_message("user"):
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st.markdown(prompt)
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# Display assistant response in chat message container
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with st.chat_message("assistant"):
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message_placeholder = st.empty()
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full_response = ""
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streaming_response = query_engine.query(prompt)
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for chunk in streaming_response:
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full_response += chunk
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message_placeholder.markdown(full_response + "▌")
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time.sleep(0.01)
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message_placeholder.markdown(full_response)
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# Add assistant response to chat history
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st.session_state.messages.append({"role": "assistant", "content": full_response}) |