Create app.py

app.py

@@ -0,0 +1,141 @@
+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}
+        )