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Delete bad_app.py
Browse files- bad_app.py +0 -237
bad_app.py
DELETED
@@ -1,237 +0,0 @@
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import sys
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import os
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import re
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import shutil
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import time
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import streamlit as st
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import nltk
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import tempfile
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import subprocess
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import base64 # For embedding PDF content
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# Pin NLTK to version 3.9.1
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REQUIRED_NLTK_VERSION = "3.9.1"
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subprocess.run([sys.executable, "-m", "pip", "install", f"nltk=={REQUIRED_NLTK_VERSION}"])
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# Set up temporary directory for NLTK resources
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nltk_data_path = os.path.join(tempfile.gettempdir(), "nltk_data")
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os.makedirs(nltk_data_path, exist_ok=True)
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nltk.data.path.append(nltk_data_path)
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# Download 'punkt_tab' for compatibility
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try:
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print("Ensuring NLTK 'punkt_tab' resource is downloaded...")
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nltk.download("punkt_tab", download_dir=nltk_data_path)
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except Exception as e:
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print(f"Error downloading NLTK 'punkt_tab': {e}")
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raise e
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sys.path.append(os.path.abspath("."))
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from langchain.chains import ConversationalRetrievalChain
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from langchain.memory import ConversationBufferMemory
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from langchain.llms import OpenAI
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from langchain.document_loaders import UnstructuredPDFLoader
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from langchain.vectorstores import Chroma
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from langchain.embeddings import HuggingFaceEmbeddings
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from langchain.text_splitter import NLTKTextSplitter
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from patent_downloader import PatentDownloader
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PERSISTED_DIRECTORY = tempfile.mkdtemp()
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# Fetch API key securely from the environment
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OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
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if not OPENAI_API_KEY:
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st.error("Critical Error: OpenAI API key not found in the environment variables. Please configure it.")
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st.stop()
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def check_poppler_installed():
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if not shutil.which("pdfinfo"):
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raise EnvironmentError(
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"Poppler is not installed or not in PATH. Install 'poppler-utils' for PDF processing."
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)
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check_poppler_installed()
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def load_docs(document_path):
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try:
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loader = UnstructuredPDFLoader(
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document_path,
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mode="elements",
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strategy="fast",
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ocr_languages=None
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)
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documents = loader.load()
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text_splitter = NLTKTextSplitter(chunk_size=1000)
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split_docs = text_splitter.split_documents(documents)
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# Filter metadata to only include str, int, float, or bool
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for doc in split_docs:
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if hasattr(doc, "metadata") and isinstance(doc.metadata, dict):
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doc.metadata = {
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k: v for k, v in doc.metadata.items()
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if isinstance(v, (str, int, float, bool))
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}
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return split_docs
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except Exception as e:
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st.error(f"Failed to load and process PDF: {e}")
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st.stop()
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def already_indexed(vectordb, file_name):
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indexed_sources = set(
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x["source"] for x in vectordb.get(include=["metadatas"])["metadatas"]
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)
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return file_name in indexed_sources
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def load_chain(file_name=None):
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loaded_patent = st.session_state.get("LOADED_PATENT")
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vectordb = Chroma(
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persist_directory=PERSISTED_DIRECTORY,
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embedding_function=HuggingFaceEmbeddings(),
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)
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if loaded_patent == file_name or already_indexed(vectordb, file_name):
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st.write("✅ Already indexed.")
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else:
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vectordb.delete_collection()
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docs = load_docs(file_name)
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st.write("🔍 Number of Documents: ", len(docs))
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vectordb = Chroma.from_documents(
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docs, HuggingFaceEmbeddings(), persist_directory=PERSISTED_DIRECTORY
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)
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vectordb.persist()
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st.session_state["LOADED_PATENT"] = file_name
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memory = ConversationBufferMemory(
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memory_key="chat_history",
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return_messages=True,
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input_key="question",
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output_key="answer",
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)
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return ConversationalRetrievalChain.from_llm(
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OpenAI(temperature=0, openai_api_key=OPENAI_API_KEY),
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vectordb.as_retriever(search_kwargs={"k": 3}),
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return_source_documents=False,
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memory=memory,
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)
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def extract_patent_number(url):
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pattern = r"/patent/([A-Z]{2}\d+)"
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match = re.search(pattern, url)
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return match.group(1) if match else None
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def download_pdf(patent_number):
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try:
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patent_downloader = PatentDownloader(verbose=True)
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output_path = patent_downloader.download(patents=patent_number, output_path=tempfile.gettempdir())
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return output_path[0]
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except Exception as e:
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st.error(f"Failed to download patent PDF: {e}")
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st.stop()
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def embed_pdf(file_path):
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"""Convert PDF file to base64 and embed it in an iframe."""
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with open(file_path, "rb") as f:
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base64_pdf = base64.b64encode(f.read()).decode("utf-8")
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pdf_display = f"""
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<iframe src="data:application/pdf;base64,{base64_pdf}" width="700" height="1000" style="border: none;"></iframe>
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"""
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return pdf_display
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if __name__ == "__main__":
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st.set_page_config(
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page_title="Patent Chat: Google Patents Chat Demo",
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page_icon="📖",
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layout="wide",
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initial_sidebar_state="expanded",
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)
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st.header("���� Patent Chat: Google Patents Chat Demo")
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# Fetch query parameters safely
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query_params = st.query_params
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default_patent_link = query_params.get("patent_link", "https://patents.google.com/patent/US8676427B1/en")
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# Input for Google Patent Link
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patent_link = st.text_area("Enter Google Patent Link:", value=default_patent_link, height=100)
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# Button to start processing
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if st.button("Load and Process Patent"):
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if not patent_link:
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st.warning("Please enter a Google patent link to proceed.")
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st.stop()
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patent_number = extract_patent_number(patent_link)
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if not patent_number:
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st.error("Invalid patent link format. Please provide a valid Google patent link.")
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st.stop()
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st.write(f"Patent number: **{patent_number}**")
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# Define PDF path in temp directory
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pdf_path = os.path.join(tempfile.gettempdir(), f"{patent_number}.pdf")
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if os.path.isfile(pdf_path):
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st.write("✅ File already downloaded.")
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else:
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st.write("📥 Downloading patent file...")
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pdf_path = download_pdf(patent_number)
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st.write(f"✅ File downloaded: {pdf_path}")
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# Display a preview of the downloaded PDF
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st.write("📄 Preview of the downloaded patent PDF:")
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if os.path.isfile(pdf_path):
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with open(pdf_path, "rb") as pdf_file:
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st.download_button(
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label="Download PDF",
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data=pdf_file,
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file_name=f"{patent_number}.pdf",
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mime="application/pdf"
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)
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# Embed PDF content using base64
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st.write("📋 PDF Content:")
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pdf_view = embed_pdf(pdf_path)
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st.components.v1.html(pdf_view, height=1000)
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st.write("🔄 Loading document into the system...")
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# Persist the chain in session state to prevent reloading
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if "chain" not in st.session_state or st.session_state.get("loaded_file") != pdf_path:
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st.session_state.chain = load_chain(pdf_path)
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st.session_state.loaded_file = pdf_path
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st.session_state.messages = [{"role": "assistant", "content": "Hello! How can I assist you with this patent?"}]
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st.success("🚀 Document successfully loaded! You can now start asking questions.")
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# Initialize messages if not already done
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if "messages" not in st.session_state:
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st.session_state.messages = [{"role": "assistant", "content": "Hello! How can I assist you with this patent?"}]
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# Display previous chat messages
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for message in st.session_state.messages:
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with st.chat_message(message["role"]):
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st.markdown(message["content"])
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# Chat Input Section
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if "chain" in st.session_state:
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if user_input := st.chat_input("What is your question?"):
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# Append user message
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st.session_state.messages.append({"role": "user", "content": user_input})
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with st.chat_message("user"):
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st.markdown(user_input)
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# Generate assistant response
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with st.chat_message("assistant"):
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message_placeholder = st.empty()
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full_response = ""
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with st.spinner("Generating response..."):
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try:
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assistant_response = st.session_state.chain({"question": user_input})
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full_response = assistant_response["answer"]
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except Exception as e:
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full_response = f"An error occurred: {e}"
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# Display assistant response
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message_placeholder.markdown(full_response)
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st.session_state.messages.append({"role": "assistant", "content": full_response})
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else:
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st.info("Press the 'Load and Process Patent' button to start processing.")
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