app.py

import os
import gc
import tempfile
import uuid
import pandas as pd
import streamlit as st
from llama_index.core import Settings
from llama_index.llms.cerebras import Cerebras
from llama_index.core import PromptTemplate
from llama_index.embeddings.mixedbreadai import MixedbreadAIEmbedding, EncodingFormat
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.readers.docling import DoclingReader
from llama_index.core.node_parser import MarkdownNodeParser
from llama_index.core.llms import ChatMessage

if "id" not in st.session_state:
    st.session_state.id = uuid.uuid4()
    st.session_state.file_cache = {}

session_id = st.session_state.id
client = None

# Initialize Cerebras LLM (ensure it is available across the app)
def load_llm():
    # Ensure you have the API Key set in your environment or via input
    api_key = os.getenv("CEREBRAS_API_KEY")
    llm = Cerebras(model="llama-3.3-70b", api_key=api_key)
    return llm

def reset_chat():
    st.session_state.messages = []
    st.session_state.context = None
    gc.collect()

def display_excel(file):
    st.markdown("### Excel Preview")
    # Read the Excel file
    df = pd.read_excel(file)
    # Display the dataframe
    st.dataframe(df)

with st.sidebar:
    st.header(f"Add your documents!")

    uploaded_file = st.file_uploader("Choose your `.xlsx` file", type=["xlsx", "xls"])

    if uploaded_file:
        try:
            with tempfile.TemporaryDirectory() as temp_dir:
                file_path = os.path.join(temp_dir, uploaded_file.name)

                with open(file_path, "wb") as f:
                    f.write(uploaded_file.getvalue())

                file_key = f"{session_id}-{uploaded_file.name}"
                st.write("Indexing your document...")

                if file_key not in st.session_state.get('file_cache', {}):
                    if os.path.exists(temp_dir):
                        reader = DoclingReader()
                        loader = SimpleDirectoryReader(
                            input_dir=temp_dir,
                            file_extractor={".xlsx": reader},
                        )
                    else:
                        st.error('Could not find the file you uploaded, please check again...')
                        st.stop()

                    docs = loader.load_data()

                    # Setup llm & embedding model
                    llm = load_llm()
                    # Use Mixedbread AI Embedding instead of HuggingFaceEmbedding
                    mixedbread_api_key = os.getenv("MXBAI_API_KEY")
                    embed_model = MixedbreadAIEmbedding(api_key=mixedbread_api_key, model_name="mixedbread-ai/mxbai-embed-large-v1")

                    # Creating an index over loaded data
                    Settings.embed_model = embed_model
                    node_parser = MarkdownNodeParser()
                    index = VectorStoreIndex.from_documents(documents=docs, transformations=[node_parser], show_progress=True)

                    # Create the query engine
                    Settings.llm = llm
                    query_engine = index.as_query_engine(streaming=True)

                    # Customise prompt template
                    qa_prompt_tmpl_str = (
                        "Context information is below.\n"
                        "---------------------\n"
                        "{context_str}\n"
                        "---------------------\n"
                        "Given the context information above I want you to think step by step to answer the query in a highly precise and crisp manner focused on the final answer, in case you don't know the answer say 'I don't know!'.\n"
                        "Query: {query_str}\n"
                        "Answer: "
                    )
                    qa_prompt_tmpl = PromptTemplate(qa_prompt_tmpl_str)

                    query_engine.update_prompts(
                        {"response_synthesizer:text_qa_template": qa_prompt_tmpl}
                    )

                    st.session_state.file_cache[file_key] = query_engine
                else:
                    query_engine = st.session_state.file_cache[file_key]

                # Inform the user that the file is processed and Display the Excel uploaded
                st.success("Ready to Chat!")
                display_excel(uploaded_file)

        except Exception as e:
            st.error(f"An error occurred: {e}")
            st.stop()

col1, col2 = st.columns([6, 1])

with col1:
    st.header(f"RAG over Excel using DuckLink 🐥 & Llama-3.3-70B")

with col2:
    st.button("Clear ↺", on_click=reset_chat)

# Initialize chat history
if "messages" not in st.session_state:
    reset_chat()

# Display chat messages from history on app rerun
for message in st.session_state.messages:
    with st.chat_message(message["role"]):
        st.markdown(message["content"])

# Accept user input
if prompt := st.chat_input("What's up?"):
    # Add user message to chat history
    st.session_state.messages.append({"role": "user", "content": prompt})
    # Display user message in chat message container
    with st.chat_message("user"):
        st.markdown(prompt)

    # Display assistant response in chat message container
    with st.chat_message("assistant"):
        message_placeholder = st.empty()
        full_response = ""
        
        # Simulate stream of response with milliseconds delay
        streaming_response = query_engine.query(prompt)
        
        for chunk in streaming_response.response_gen:
            full_response += chunk
            message_placeholder.markdown(full_response + "▌")

        message_placeholder.markdown(full_response)

    # Add assistant response to chat history
    st.session_state.messages.append({"role": "assistant", "content": full_response})