Delete app1.py

app1.py

@@ -1,107 +0,0 @@
-import streamlit as st
-from PyPDF2 import PdfReader
-from langchain.text_splitter import CharacterTextSplitter, RecursiveCharacterTextSplitter
-import os, getpass
-from langchain_google_genai import GoogleGenerativeAIEmbeddings
-import google.generativeai as genai
-from langchain.vectorstores import FAISS
-from langchain_google_genai import ChatGoogleGenerativeAI
-from langchain.chains.question_answering import load_qa_chain
-from langchain.prompts import PromptTemplate
-from langchain.chains import ConversationalRetrievalChain
-from langchain.memory import ConversationBufferMemory
-
-
-#Gemini Key
-genai.configure(api_key=os.environ['GOOGLE_API_KEY'])
-
-
-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_text_chunks(text):
-    #RecursiveCharacterTextSplitter CharacterTextSplitter separator="\n", 
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=1000, length_function=len)#
-    chunks = text_splitter.split_text(text)
-    return chunks
-
-
-def get_vector_store(text_chunks):
-    embeddings = GoogleGenerativeAIEmbeddings(model = "models/embedding-001")
-    vector_store = FAISS.from_texts(text_chunks, embedding=embeddings)
-    return vector_store
-
-
-def get_conversational_chain(Fvs):
-
-    prompt_template = """
-    Answer the question as detailed as possible from the provided context, make sure to provide all the details, if the answer is not in
-    provided context just say, "answer is not available in the context", don't provide the wrong answer\n\n
-    Context:\n {context}?\n
-    Question: \n{question}\n
-
-    Answer:
-    """
-
-    model = ChatGoogleGenerativeAI(model="gemini-1.5-pro",temperature=0.3)
-    prompt = PromptTemplate(template = prompt_template, input_variables = ["context", "question"])
-    memory = ConversationBufferMemory(memory_key = "chat_history", return_messages=True)
-    chain = ConversationalRetrievalChain.from_llm(llm=model,retriever=Fvs.as_retriever(), memory=memory)
-
-    return chain
-
-
-
-def user_input(user_question):
-    response = st.session_state.conversation({'question': user_question})
-    st.session_state.chatHistory = response['chat_history']
-    for i, message in enumerate(st.session_state.chatHistory):
-        if i%2 == 0:
-            st.write("Human: ", message.content)
-        else:
-            st.write("Bot: ", message.content)
-
-## streamlit app
-st.set_page_config("Chat With Multiple PDF")
-st.header("Chat with Multiple PDF :books:")
-
-user_question = st.text_input("Ask a Question from the PDF Files")
-submit=st.button("Ask the question")
-
-## If ask button is clicked
-if submit:
-    if "conversation" not in st.session_state:
-        st.session_state.conversation = None
-    if "chatHistory" not in st.session_state:
-        st.session_state.chatHistory = None
-    if user_question:
-        user_input(user_question)
-
-with st.sidebar:
-    st.title("Menu:")
-    pdf_docs = st.file_uploader("Upload your PDF Files", accept_multiple_files=True)
-    if st.button("Submit & Process"):
-         with st.spinner("Processing..."):
-            raw_text = get_pdf_text(pdf_docs)
-            text_chunks = get_text_chunks(raw_text)
-            Fvs = get_vector_store(text_chunks)
-            st.session_state.conversation = get_conversational_chain(Fvs)
-            st.success("Done")
-          
-    if st.button("Clear Chat Window", use_container_width=True, type="primary"):
-        st.session_state.history = []
-        st.rerun()
-
-footer = """
----
-#### Made By [Surat Banerjee](https://www.linkedin.com/in/surat-banerjee/)
-For Any Queries, Reach out on [Portfolio](https://suratbanerjee.wixsite.com/myportfoliods)  
-"""
-
-st.markdown(footer, unsafe_allow_html=True)