app.py

import streamlit as st
import os
import nest_asyncio
import re
from pathlib import Path
import typing as t
import base64
from mimetypes import guess_type
from llama_parse import LlamaParse
from llama_index.core.schema import TextNode
from llama_index.core import VectorStoreIndex, StorageContext, load_index_from_storage, Settings
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.llms.openai import OpenAI
from llama_index.core.query_engine import CustomQueryEngine
from llama_index.multi_modal_llms.openai import OpenAIMultiModal
from llama_index.core.prompts import PromptTemplate
from llama_index.core.schema import ImageNode
from llama_index.core.base.response.schema import Response
from typing import Optional, List

nest_asyncio.apply()

# Setting API keys
os.environ["OPENAI_API_KEY"] = os.getenv('OPENAI_API_KEY')
os.environ["LLAMA_CLOUD_API_KEY"] = os.getenv('LLAMA_CLOUD_API_KEY')

# Initialize Streamlit app
st.title("Medical Knowledge Base & Query System")
st.sidebar.title("Settings")

# User input for file upload
st.sidebar.subheader("Upload Knowledge Base")
uploaded_file = st.sidebar.file_uploader("Upload a medical text book (pdf)", type=["jpg", "png", "pdf"])

# # Ensure the 'files' directory exists
# if not os.path.exists("files"):
#     os.makedirs("files")
    
# Initialize the parser
parser = LlamaParse(
    result_type="markdown",
    parsing_instruction="You are given a medical textbook on medicine",
    use_vendor_multimodal_model=True,
    vendor_multimodal_model_name="gpt-4o-mini-2024-07-18",
    show_progress=True,
    verbose=True,
    invalidate_cache=True,
    do_not_cache=True,
    num_workers=8,
    language="en"
)

# Initialize md_json_objs as an empty list
md_json_objs = []

# Upload and process file
if uploaded_file:
    st.sidebar.write("Processing file...")
    file_path = f"{uploaded_file.name}"
    with open(file_path, "wb") as f:
        f.write(uploaded_file.read())

    # Parse the uploaded image
    md_json_objs = parser.get_json_result([file_path])
    image_dicts = parser.get_images(md_json_objs, download_path="data_images")
    
    # Extract and display parsed information
    st.write("File successfully processed!")
    st.write(f"Processed file: {uploaded_file.name}")

# Function to encode image to data URL
def local_image_to_data_url(image_path):
    mime_type, _ = guess_type(image_path)
    if mime_type is None:
        mime_type = 'image/png'
    with open(image_path, "rb") as image_file:
        base64_encoded_data = base64.b64encode(image_file.read()).decode('utf-8')
    return f"data:{mime_type};base64,{base64_encoded_data}"

# Function to get sorted image files
def get_page_number(file_name):
    match = re.search(r"-page-(\d+)\.jpg$", str(file_name))
    if match:
        return int(match.group(1))
    return 0

def _get_sorted_image_files(image_dir):
    raw_files = [f for f in list(Path(image_dir).iterdir()) if f.is_file()]
    sorted_files = sorted(raw_files, key=get_page_number)
    return sorted_files

def get_text_nodes(md_json_objs, image_dir) -> t.List[TextNode]:
    nodes = []
    for result in md_json_objs:
        json_dicts = result["pages"]
        document_name = result["file_path"].split('/')[-1]
        docs = [doc["md"] for doc in json_dicts]
        image_files = _get_sorted_image_files(image_dir)
        for idx, doc in enumerate(docs):
            node = TextNode(
                text=doc,
                metadata={"image_path": str(image_files[idx]), "page_num": idx + 1, "document_name": document_name},
            )
            nodes.append(node)
    return nodes

# Load text nodes if md_json_objs is not empty
if md_json_objs:
    text_nodes = get_text_nodes(md_json_objs, "data_images")
else:
    text_nodes = []

# Setup index and LLM
embed_model = OpenAIEmbedding(model="text-embedding-3-large")
llm = OpenAI("gpt-4o-mini-2024-07-18")
Settings.llm = llm
Settings.embed_model = embed_model

if not os.path.exists("storage_manuals"):
    index = VectorStoreIndex(text_nodes, embed_model=embed_model)
    index.storage_context.persist(persist_dir="./storage_manuals")
else:
    ctx = StorageContext.from_defaults(persist_dir="./storage_manuals")
    index = load_index_from_storage(ctx)

retriever = index.as_retriever()

# Query input
st.subheader("Ask a Question")
query_text = st.text_input("Enter your query:")
uploaded_query_image = st.file_uploader("Upload a query image (if any):", type=["jpg", "png"])

# Encode query image if provided
encoded_image_url = None
if uploaded_query_image:
    query_image_path = f"{uploaded_query_image.name}"
    with open(query_image_path, "wb") as img_file:
        img_file.write(uploaded_query_image.read())
    encoded_image_url = local_image_to_data_url(query_image_path)

# Setup query engine
QA_PROMPT_TMPL = """
You are a friendly medical chatbot designed to assist users by providing accurate and detailed responses to medical questions based on information from medical books.

### Context:
---------------------
{context_str}
---------------------

### Query Text:
{query_str}

### Query Image:
---------------------
{encoded_image_url}
---------------------

### Answer:
"""
QA_PROMPT = PromptTemplate(QA_PROMPT_TMPL)
gpt_4o_mm = OpenAIMultiModal(model="gpt-4o-mini-2024-07-18")

class MultimodalQueryEngine(CustomQueryEngine):
    def __init__(self, qa_prompt, retriever, multi_modal_llm, node_postprocessors=[]):
        super().__init__(qa_prompt=qa_prompt, retriever=retriever, multi_modal_llm=multi_modal_llm, node_postprocessors=node_postprocessors)

    def custom_query(self, query_str):
        nodes = self.retriever.retrieve(query_str)
        image_nodes = [NodeWithScore(node=ImageNode(image_path=n.node.metadata["image_path"])) for n in nodes]
        ctx_str = "\n\n".join([r.node.get_content().strip() for r in nodes])
        fmt_prompt = self.qa_prompt.format(context_str=ctx_str, query_str=query_str, encoded_image_url=encoded_image_url)
        llm_response = self.multi_modal_llm.complete(prompt=fmt_prompt, image_documents=[image_node.node for image_node in image_nodes])
        return Response(response=str(llm_response), source_nodes=nodes, metadata={"text_nodes": text_nodes, "image_nodes": image_nodes})

query_engine = MultimodalQueryEngine(QA_PROMPT, retriever, gpt_4o_mm)

# Handle query
if query_text:
    st.write("Querying...")
    response = query_engine.custom_query(query_text)
    st.markdown(response.response)