Delete app.py

app.py

@@ -1,70 +0,0 @@
-from cgitb import text
-import streamlit as st
-from dotenv import load_dotenv
-from PyPDF2 import PdfReader
-from langchain_text_splitters import CharacterTextSplitter
-from langchain.embeddings import HuggingFaceInstructEmbeddings
-from langchain.vectorstores import FAISS 
-from langchain.memory import ConversationalBufferMemory
-from langchain.chains import ConversationalRetrievalChain
-from langchain.llms import HuggingFaceHub
-
-
-def get_text_chunks(raw_text):
-    text_splitter = CharacterTextSplitter(
-        separator="\n",
-        chunk_size=1000, chunk_overlap=200,
-        length_function= len
-    )
-    chunks = text_splitter.split(text)
-    return chunks
-    
-def get_pdf_text(pdf_docs):
-    text =""
-    for pdf in pdf_docs:
-        pdf_reader = PdfReader(pdf)
-        for page in pdf_reader.pages:
-            text += page.extract_text()
-    return text 
-
-def get_vectorestore(text_chunks):
-    embeddings = HuggingFaceInstructEmbeddings(model_name= "hkunlp/instructor-xl")
-    vectorestore = FAISS.from_texts(texts=text_chunks, embedding= embeddings)
-    return vectorestore
-         
-def get_conversation_chain(vectorstore):
-     llm = HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature":0.5, "max_length":512})
-     memory = ConversationalBufferMemory(memory_key="chat_history", return_message= True)
-     conversation_chain = ConversationalRetrievalChain.from_llm(
-             llm=llm,
-             retriever=vectorstore.as_retriever(),
-             memory= memory
-         )
-     return get_conversation_chain
-     
-def main():
-    load_dotenv()
-    st.set_page_config(page_title="Chat with your PDFs", page_icon= ":books:")
-    
-    if "conversation" not in st.session_state:
-        st.session_state.conversation = None
-        
-    st.header("Chat with your PDFs :books:")
-    st.text_input("Ask a question about your documents:")
-    with st.sidebar:
-        st.subheader("Your Documemts")
-        pdf_docs = st.file_uploader("Upload your PDFs here", accept_multiple_files= True)
-        if st.button("Enter"):
-            with st.spinner("Processing"):
-            # get pdf text
-                raw_text = get_pdf_text(pdf_docs)
-            # get text chunks
-                text_chunks = get_text_chunks(raw_text)
-            # create vector store
-                vectorstore = get_vectorstore(text_chunks)
-                
-            # conversation_chain
-                st.session_state.conversation = get_conversation_chain(vectorstore)
-    st.session_state.conversation    
-if __name__ == "__main__":
-    main()