Delete app.py

app.py

@@ -1,213 +0,0 @@
-# import streamlit as st
-# from PyPDF2 import PdfReader
-# from langchain.text_splitter import RecursiveCharacterTextSplitter
-# import os
-# 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 dotenv import load_dotenv
-
-# load_dotenv()
-# os.getenv("GOOGLE_API_KEY")
-# genai.configure(api_key=os.getenv("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):
-#     text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=1000)
-#     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)
-#     vector_store.save_local("faiss_index",allow_dangerous_deserialization=True)
-
-
-# def get_conversational_chain():
-
-#     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-pro",
-#                              temperature=0.3)
-
-#     prompt = PromptTemplate(template = prompt_template, input_variables = ["context", "question"])
-#     chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
-
-#     return chain
-
-
-
-# def user_input(user_question):
-#     embeddings = GoogleGenerativeAIEmbeddings(model = "models/embedding-001")
-    
-#     new_db = FAISS.load_local("faiss_index", embeddings)
-#     docs = new_db.similarity_search(user_question)
-
-#     chain = get_conversational_chain()
-
-    
-#     response = chain(
-#         {"input_documents":docs, "question": user_question}
-#         , return_only_outputs=True)
-
-#     print(response)
-#     st.write("Reply: ", response["output_text"])
-
-
-
-
-# def main():
-#     st.set_page_config("Chat PDF")
-#     st.header("Chat with PDF using Gemini💁")
-
-#     user_question = st.text_input("Ask a Question from the PDF Files")
-
-#     if user_question:
-#         user_input(user_question)
-
-#     with st.sidebar:
-#         st.title("Menu:")
-#         pdf_docs = st.file_uploader("Upload your PDF Files and Click on the Submit & Process Button", 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)
-#                 get_vector_store(text_chunks)
-#                 st.success("Done")
-
-
-
-# if __name__ == "__main__":
-#     main()
-
-import streamlit as st
-from dotenv import load_dotenv
-# import PyPDF2
-from PyPDF2 import PdfReader
-from langchain.text_splitter import CharacterTextSplitter
-from langchain.embeddings import OpenAIEmbeddings, HuggingFaceInstructEmbeddings
-from langchain_community.document_loaders import PyMuPDFLoader
-from langchain.vectorstores import FAISS
-from langchain.chat_models import ChatOpenAI
-from langchain.memory import ConversationBufferMemory
-from langchain.chains import ConversationalRetrievalChain
-from htmlTemplates import css, bot_template, user_template
-from langchain.llms import HuggingFaceHub
-
-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):
-    text_splitter = CharacterTextSplitter(
-        separator="\n",
-        chunk_size=1000,
-        chunk_overlap=200,
-        length_function=len
-    )
-    chunks = text_splitter.split_text(text)
-    return chunks
-
-
-def get_vectorstore(text_chunks):
-    embeddings = OpenAIEmbeddings()
-    # embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl")
-    vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
-    return vectorstore
-
-
-def get_conversation_chain(vectorstore):
-    llm = ChatOpenAI()
-    # llm = HuggingFaceHub(repo_id="google/flan-t5-xxl", 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=vectorstore.as_retriever(),
-        memory=memory
-    )
-    return conversation_chain
-
-
-def handle_userinput(user_question):
-    response = st.session_state.conversation({'question': user_question})
-    st.session_state.chat_history = response['chat_history']
-
-    for i, message in enumerate(st.session_state.chat_history):
-        if i % 2 == 0:
-            st.write(user_template.replace(
-                "{{MSG}}", message.content), unsafe_allow_html=True)
-        else:
-            st.write(bot_template.replace(
-                "{{MSG}}", message.content), unsafe_allow_html=True)
-
-
-def main():
-    load_dotenv()
-    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
-    if "chat_history" not in st.session_state:
-        st.session_state.chat_history = None
-
-    st.header("Chat with multiple PDFs :books:")
-    user_question = st.text_input("Ask a question about your documents:")
-    if user_question:
-        handle_userinput(user_question)
-
-    with st.sidebar:
-        st.subheader("Your documents")
-        pdf_docs = st.file_uploader(
-            "Upload your PDFs here and click on 'Process'", accept_multiple_files=True)
-        if st.button("Process"):
-            with st.spinner("Processing"):
-                # get pdf text
-                raw_text = get_pdf_text(pdf_docs)
-
-                # get the text chunks
-                text_chunks = get_text_chunks(raw_text)
-
-                # create vector store
-                vectorstore = get_vectorstore(text_chunks)
-
-                # create conversation chain
-                st.session_state.conversation = get_conversation_chain(
-                    vectorstore)
-
-
-if __name__ == '__main__':
-    main()