Update app.py

app.py

@@ -21,106 +21,6 @@ st.write(
 )
 
 
-@st.cache_resource
-def initialize_resources():
-    llm_gemini = ChatGoogleGenerativeAI(
-        model="gemini-1.5-flash-latest", google_api_key=os.getenv("GOOGLE_API_KEY")
-    )
-    return llm_gemini
-
-#@st.cache_data
-def get_retriever(pdf_file):
-    with NamedTemporaryFile(suffix="pdf") as temp:
-        temp.write(pdf_file.getvalue())
-        pdf_loader = PyPDFLoader(temp.name, extract_images=True)
-        pages = pdf_loader.load()
-
-    st.write(f"AI Chatbot for {course_material}")
-
-    underlying_embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
-    text_splitter = RecursiveCharacterTextSplitter(
-        chunk_size=1000,
-        chunk_overlap=20,
-        length_function=len,
-        is_separator_regex=False,
-        separators="\n",
-    )
-    documents = text_splitter.split_documents(pages)
-    vectorstore = faiss.FAISS.from_documents(documents, underlying_embeddings)
-    doc_retiever = vectorstore.as_retriever()
-       # search_type="mmr", search_kwargs={"k": 5, "fetch_k": 10}
-    #)
-
-    return doc_retiever
-
-
-chat_model = initialize_resources()
-
-# Streamlit UI
-# Course list and pdf retrieval
-
-courses = ["PMB", "PCL", "Kelechi_research"]  # "GSP", "CPM", "PCG",  "PCH"
-course_pdfs = None
-doc_retriever = None
-conversational_chain = None
-
-# course = st.sidebar.selectbox("Choose course", (courses))
-# docs_path = f"pdfs/{course}"
-# course_pdfs = os.listdir(docs_path)
-# pdfs = [os.path.join(docs_path, pdf) for pdf in course_pdfs]
-
-course_material = "{Not selected}"
-
-
-# @st.cache_resource
-def query_response(query, _retriever):
-    memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
-    conversational_chain = ConversationalRetrievalChain.from_llm(
-        llm=chat_model, retriever=_retriever, memory=memory, verbose=False
-    )
-    response = conversational_chain.run(query)
-
-    return response
-
-
-if "doc" not in st.session_state:
-    st.session_state.doc = ""
-
-course_material = st.file_uploader("or Upload your own pdf", type="pdf")
-
-if st.session_state != "":
-    try:
-        if st.button("load"):
-            with st.spinner("loading document.."):
-                doc_retriever = get_retriever(course_material)
-                
-            st.success("File loading successful, vector db initialize")
-    except Exception as e:
-        st.error(e)
-
-import os
-from langchain_community.document_loaders import PyPDFLoader
-from langchain_community.vectorstores import faiss
-from langchain.memory import ConversationBufferMemory
-from langchain_google_genai import ChatGoogleGenerativeAI, GoogleGenerativeAIEmbeddings
-from tempfile import NamedTemporaryFile
-from dotenv import load_dotenv
-from langchain_text_splitters import RecursiveCharacterTextSplitter
-from langchain.chains import ConversationalRetrievalChain
-import streamlit as st
-import nest_asyncio
-
-nest_asyncio.apply()
-load_dotenv()
-
-# Initialize app resources
-st.set_page_config(page_title="StudyAssist", page_icon=":book:")
-st.title("StudyAssist(pharmassist-v0)")
-st.write(
-    "An AI/RAG application to aid students in their studies, specially optimized for the pharm 028 students. In simpler terms, chat with your pdf"
-)
-
-
 @st.cache_resource
 def initialize_resources():
     llm_gemini = ChatGoogleGenerativeAI(