app.py

import sys
import os
import re
import shutil
import time
import streamlit as st
import nltk
import tempfile
import subprocess
import base64  # For embedding PDF content

# Pin NLTK to version 3.9.1
REQUIRED_NLTK_VERSION = "3.9.1"
subprocess.run([sys.executable, "-m", "pip", "install", f"nltk=={REQUIRED_NLTK_VERSION}"])

# Set up temporary directory for NLTK resources
nltk_data_path = os.path.join(tempfile.gettempdir(), "nltk_data")
os.makedirs(nltk_data_path, exist_ok=True)
nltk.data.path.append(nltk_data_path)

# Download 'punkt_tab' for compatibility
try:
    print("Ensuring NLTK 'punkt_tab' resource is downloaded...")
    nltk.download("punkt_tab", download_dir=nltk_data_path)
except Exception as e:
    print(f"Error downloading NLTK 'punkt_tab': {e}")
    raise e

sys.path.append(os.path.abspath("."))
from langchain.chains import ConversationalRetrievalChain
from langchain.memory import ConversationBufferMemory
from langchain.llms import OpenAI
from langchain.document_loaders import UnstructuredPDFLoader
from langchain.vectorstores import Chroma
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.text_splitter import NLTKTextSplitter
from patent_downloader import PatentDownloader

PERSISTED_DIRECTORY = tempfile.mkdtemp()

# Fetch API key securely from the environment
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
if not OPENAI_API_KEY:
    st.error("Critical Error: OpenAI API key not found in the environment variables. Please configure it.")
    st.stop()

def check_poppler_installed():
    if not shutil.which("pdfinfo"):
        raise EnvironmentError(
            "Poppler is not installed or not in PATH. Install 'poppler-utils' for PDF processing."
        )

check_poppler_installed()

def load_docs(document_path):
    try:
        loader = UnstructuredPDFLoader(
            document_path,
            mode="elements",
            strategy="fast",
            ocr_languages=None
        )
        documents = loader.load()
        text_splitter = NLTKTextSplitter(chunk_size=1000)
        split_docs = text_splitter.split_documents(documents)
        
        # Filter metadata to only include str, int, float, or bool
        for doc in split_docs:
            if hasattr(doc, "metadata") and isinstance(doc.metadata, dict):
                doc.metadata = {
                    k: v for k, v in doc.metadata.items()
                    if isinstance(v, (str, int, float, bool))
                }
        return split_docs
    except Exception as e:
        st.error(f"Failed to load and process PDF: {e}")
        st.stop()

def already_indexed(vectordb, file_name):
    indexed_sources = set(
        x["source"] for x in vectordb.get(include=["metadatas"])["metadatas"]
    )
    return file_name in indexed_sources

def load_chain(file_name=None):
    loaded_patent = st.session_state.get("LOADED_PATENT")

    vectordb = Chroma(
        persist_directory=PERSISTED_DIRECTORY,
        embedding_function=HuggingFaceEmbeddings(),
    )
    if loaded_patent == file_name or already_indexed(vectordb, file_name):
        st.write("✅ Already indexed.")
    else:
        vectordb.delete_collection()
        docs = load_docs(file_name)
        st.write("🔍 Number of Documents: ", len(docs))

        vectordb = Chroma.from_documents(
            docs, HuggingFaceEmbeddings(), persist_directory=PERSISTED_DIRECTORY
        )
        vectordb.persist()
        st.session_state["LOADED_PATENT"] = file_name

    memory = ConversationBufferMemory(
        memory_key="chat_history",
        return_messages=True,
        input_key="question",
        output_key="answer",
    )
    return ConversationalRetrievalChain.from_llm(
        OpenAI(temperature=0, openai_api_key=OPENAI_API_KEY),
        vectordb.as_retriever(search_kwargs={"k": 3}),
        return_source_documents=False,
        memory=memory,
    )

def extract_patent_number(url):
    pattern = r"/patent/([A-Z]{2}\d+)"
    match = re.search(pattern, url)
    return match.group(1) if match else None

def download_pdf(patent_number):
    try:
        patent_downloader = PatentDownloader(verbose=True)
        output_path = patent_downloader.download(patents=patent_number, output_path=tempfile.gettempdir())
        return output_path[0]
    except Exception as e:
        st.error(f"Failed to download patent PDF: {e}")
        st.stop()

def embed_pdf(file_path):
    """Convert PDF file to base64 and embed it in an iframe."""
    with open(file_path, "rb") as f:
        base64_pdf = base64.b64encode(f.read()).decode("utf-8")
    pdf_display = f"""
        <iframe src="data:application/pdf;base64,{base64_pdf}" width="700" height="1000" style="border: none;"></iframe>
    """
    return pdf_display

if __name__ == "__main__":
    st.set_page_config(
        page_title="Patent Chat: Google Patents Chat Demo",
        page_icon="📖",
        layout="wide",
        initial_sidebar_state="expanded",
    )
    st.header("📖 Patent Chat: Google Patents Chat Demo")

    # Fetch query parameters safely
    query_params = st.query_params
    default_patent_link = query_params.get("patent_link", "https://patents.google.com/patent/US8676427B1/en")
    
    # Input for Google Patent Link
    patent_link = st.text_area("Enter Google Patent Link:", value=default_patent_link, height=100)

    # Button to start processing
    if st.button("Load and Process Patent"):
        if not patent_link:
            st.warning("Please enter a Google patent link to proceed.")
            st.stop()

        patent_number = extract_patent_number(patent_link)
        if not patent_number:
            st.error("Invalid patent link format. Please provide a valid Google patent link.")
            st.stop()

        st.write(f"Patent number: **{patent_number}**")

        # Define PDF path in temp directory
        pdf_path = os.path.join(tempfile.gettempdir(), f"{patent_number}.pdf")
        if os.path.isfile(pdf_path):
            st.write("✅ File already downloaded.")
        else:
            st.write("📥 Downloading patent file...")
            pdf_path = download_pdf(patent_number)
            st.write(f"✅ File downloaded: {pdf_path}")

        # Display a preview of the downloaded PDF
        st.write("📄 Preview of the downloaded patent PDF:")
        if os.path.isfile(pdf_path):
            with open(pdf_path, "rb") as pdf_file:
                st.download_button(
                    label="Download PDF",
                    data=pdf_file,
                    file_name=f"{patent_number}.pdf",
                    mime="application/pdf"
                )
            # Embed PDF content using base64
            st.write("📋 PDF Content:")
            pdf_view = embed_pdf(pdf_path)
            st.components.v1.html(pdf_view, height=1000)

        st.write("🔄 Loading document into the system...")

        # Persist the chain in session state to prevent reloading
        if "chain" not in st.session_state or st.session_state.get("loaded_file") != pdf_path:
            st.session_state.chain = load_chain(pdf_path)
            st.session_state.loaded_file = pdf_path
            st.session_state.messages = [{"role": "assistant", "content": "Hello! How can I assist you with this patent?"}]

        st.success("🚀 Document successfully loaded! You can now start asking questions.")

    # Initialize messages if not already done
    if "messages" not in st.session_state:
        st.session_state.messages = [{"role": "assistant", "content": "Hello! How can I assist you with this patent?"}]

    # Display previous chat messages
    for message in st.session_state.messages:
        with st.chat_message(message["role"]):
            st.markdown(message["content"])

    # Chat Input Section
    if "chain" in st.session_state:
        if user_input := st.chat_input("What is your question?"):
            # Append user message
            st.session_state.messages.append({"role": "user", "content": user_input})
            with st.chat_message("user"):
                st.markdown(user_input)

            # Generate assistant response
            with st.chat_message("assistant"):
                message_placeholder = st.empty()
                full_response = ""

                with st.spinner("Generating response..."):
                    try:
                        assistant_response = st.session_state.chain({"question": user_input})
                        full_response = assistant_response["answer"]
                    except Exception as e:
                        full_response = f"An error occurred: {e}"

                # Display assistant response
                message_placeholder.markdown(full_response)
                st.session_state.messages.append({"role": "assistant", "content": full_response})
    else:
        st.info("Press the 'Load and Process Patent' button to start processing.")