test.py

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

# Ensure NLTK 'punkt' resource is downloaded
nltk_data_path = os.path.join(os.getcwd(), "nltk_data")
os.makedirs(nltk_data_path, exist_ok=True)  
nltk.data.path.append(nltk_data_path) 

# Force download of the 'punkt' resource
try:
    print("Ensuring NLTK 'punkt' resource is downloaded...")
    nltk.download("punkt", download_dir=nltk_data_path)
except Exception as e:
    print(f"Error downloading NLTK 'punkt': {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 = "."

# 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  # Explicitly disable OCR
        )
        documents = loader.load()
        text_splitter = NLTKTextSplitter(chunk_size=1000)
        return text_splitter.split_documents(documents)
    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)
        return output_path[0]  # Return the first file path
    except Exception as e:
        st.error(f"Failed to download patent PDF: {e}")
        st.stop()

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()

    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}**")

    # Download the PDF file
    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(f"✅ File downloaded: {pdf_path}")

    # Load the conversational chain
    st.write("🔄 Loading document into the system...")
    chain = load_chain(pdf_path)
    st.success("🚀 Document successfully loaded! You can now start asking questions.")

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

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

    # User input
    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)

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

        with st.spinner("Generating response..."):
            try:
                assistant_response = chain({"question": user_input})
                for chunk in assistant_response["answer"].split():
                    full_response += chunk + " "
                    time.sleep(0.05)  # Simulate typing effect
                    message_placeholder.markdown(full_response + "▌")
            except Exception as e:
                full_response = f"An error occurred: {e}"
            finally:
                message_placeholder.markdown(full_response)

        st.session_state.messages.append({"role": "assistant", "content": full_response})