Delete app.py

app.py

@@ -1,103 +0,0 @@
-import streamlit as st
-from dotenv import load_dotenv
-from PyPDF2 import PdfReader
-from langchain.text_splitter import CharacterTextSplitter
-from langchain_openai import OpenAIEmbeddings
-from langchain.vectorstores import FAISS
-# from langchain_community.vectorstores import FAISS
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.memory import ConversationBufferMemory
-from langchain.chains import ConversationalRetrievalChain
-from langchain.chat_models import ChatOpenAI
-from htmlTemplates import css, bot_template, user_template
-from langchain.embeddings import HuggingFaceInstructEmbeddings
-from langchain.llms import HuggingFaceHub
-
-def get_pdf_text(pdf_doc):
-    text = ""
-    for pdf in pdf_doc:
-        pdf_reader = PdfReader(pdf)
-        for page in pdf_reader.pages:
-            text += page.extract_text()
-    return text
-
-
-def get_text_chunk(row_text):
-    text_splitter = CharacterTextSplitter(
-        separator="\n",
-        chunk_size = 1000,
-        chunk_overlap = 200,
-        length_function = len
-    )
-    chunk = text_splitter.split_text(row_text)
-    return chunk
-
-
-def get_vectorstore(text_chunk):
-    embeddings = OpenAIEmbeddings(openai_api_key = os.getenv("OPENAI_API_KEY"))
-    # embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl")
-    vector = FAISS.from_texts(text_chunk,embeddings)
-    return vector
-
-
-def get_conversation_chain(vectorstores):
-    llm = ChatOpenAI(openai_api_key = os.getenv("OPENAI_API_KEY"))
-    # llm = HuggingFaceHub(repo_id="google/flan-t5-base", model_kwargs={"temperature":0.5, "max_length":512})
-    memory = ConversationBufferMemory(memory_key = "chat_history",return_messages = True)
-    conversation_chain = ConversationalRetrievalChain.from_llm(llm=llm,
-                                                               retriever=vectorstores.as_retriever(),
-                                                               memory=memory)
-    return conversation_chain
-
-
-def user_input(user_question):
-    response = st.session_state.conversation({"question":user_question})
-    st.session_state.chat_history = response["chat_history"]
-
-    for indx, msg in enumerate(st.session_state.chat_history):
-        if indx % 2==0:
-            st.write(user_template.replace("{{MSG}}",msg.content), unsafe_allow_html=True)
-        else:
-            st.write(bot_template.replace("{{MSG}}", msg.content), unsafe_allow_html=True)
-
-
-
-def main():
-    # load secret key
-    load_dotenv()
-    
-    # config the pg
-    st.set_page_config(page_title="Chat with multiple PDFs" ,page_icon=":books:")
-    st.write(css, unsafe_allow_html=True)
-    if "conversation" not in st.session_state:
-        st.session_state.conversation = None
-
-    st.header("Chat with multiple PDFs :books:")
-    user_question = st.text_input("Ask a question about your docs")
-    if user_question:
-        user_input(user_question)
-
-    # st.write(user_template.replace("{{MSG}}","Hello Robot"), unsafe_allow_html=True)
-    # st.write(bot_template.replace("{{MSG}}","Hello Human"), unsafe_allow_html=True)
-
-    # create side bar
-    with st.sidebar:
-        st.subheader("Your Documents")
-        pdf_doc = st.file_uploader(label="Upload your documents",accept_multiple_files=True)
-        if st.button("Process"):
-            with st.spinner(text="Processing"):
-
-            # get pdf text
-                row_text = get_pdf_text(pdf_doc)
-            # get the text chunk
-                text_chunk = get_text_chunk(row_text)
-                # st.write(text_chunk)
-            # create vecor store
-                vectorstores = get_vectorstore(text_chunk)
-                # st.write(vectorstores)
-            # create conversation chain
-                st.session_state.conversation = get_conversation_chain(vectorstores)
-
-
-if __name__ == "__main__":
-    main()