app.py

import sys
import os
import re
import streamlit as st
import time

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 = "."

# 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 load_docs(document_path):
    loader = UnstructuredPDFLoader(document_path)
    documents = loader.load()
    text_splitter = NLTKTextSplitter(chunk_size=1000)
    return text_splitter.split_documents(documents)

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("Length: ", 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):
    patent_downloader = PatentDownloader()
    patent_downloader.download(patent=patent_number)
    return f"{patent_number}.pdf"

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")
    
    # Allow user to input the Google patent link
    patent_link = st.text_input("Enter Google Patent Link:", key="PATENT_LINK")
    
    if not patent_link:
        st.warning("Please enter a Google patent link to proceed.")
        st.stop()
    else:
        st.session_state["patent_link_configured"] = True

    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("Patent number: ", patent_number)

    pdf_path = 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("File downloaded.")

    chain = load_chain(pdf_path)

    if "messages" not in st.session_state:
        st.session_state["messages"] = [
            {"role": "assistant", "content": "How can I help you?"}
        ]

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

    if user_input := st.chat_input("What is your question?"):
        st.session_state.messages.append({"role": "user", "content": user_input})
        with st.chat_message("user"):
            st.markdown(user_input)

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

        with st.spinner("CHAT-BOT is at Work ..."):
            assistant_response = chain({"question": user_input})
            for chunk in assistant_response["answer"].split():
                full_response += chunk + " "
                time.sleep(0.05)
                message_placeholder.markdown(full_response + "▌")
        st.session_state.messages.append(
            {"role": "assistant", "content": full_response}
        )