Update app.py

app.py

@@ -1,122 +0,0 @@
-# importing dependencies
-from dotenv import load_dotenv
-import streamlit as st
-from PyPDF2 import PdfReader
-from langchain.text_splitter import CharacterTextSplitter
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.vectorstores import faiss
-from langchain.prompts import PromptTemplate
-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 openai
-from langchain.embeddings.openai import OpenAIEmbeddings
-import os
-
- 
-from openai import OpenAI
-api_key = os.getenv("OPENAI_API_KEY")
-client = OpenAI(api_key=api_key)
-
-
-# creating custom template to guide llm model
-custom_template ="""<s>[INST]You will start the conversation by greeting the user and introducing yourself as qanoon-bot,\
-stating your availability for legal assistance. Your next step will depend on the user's response.\
-If the user expresses a need for legal assistance in Pakistan, you will ask them to describe their case or problem.\
-After receiving the case or problem details from the user, you will provide the solutions and procedures according to the knowledge base and also give related penal codes and procedures. \
-However, if the user does not require legal assistance in Pakistan, you will immediately thank them and\
-say goodbye, ending the conversation. Remember to base your responses on the user's needs, providing accurate and\
-concise information regarding the Pakistan legal law and rights where applicable. Your interactions should be professional and\
-focused, ensuring the user's queries are addressed efficiently without deviating from the set flows.\
-CHAT HISTORY: {chat_history}
-QUESTION: {question}
-ANSWER:
-</s>[INST]
-"""
-
-CUSTOM_QUESTION_PROMPT = PromptTemplate.from_template(custom_template)
-
-# extracting text from pdf
-def get_pdf_text(docs):
-    text=""
-    for pdf in docs:
-        pdf_reader=PdfReader(pdf)
-        for page in pdf_reader.pages:
-            text+=page.extract_text()
-    return text
-
-# converting text to chunks
-def get_chunks(raw_text):
-    text_splitter=CharacterTextSplitter(separator="\n",
-                                        chunk_size=1000,
-                                        chunk_overlap=200,
-                                        length_function=len)   
-    chunks=text_splitter.split_text(raw_text)
-    return chunks
-
-# using all-MiniLm embeddings model and faiss to get vectorstore
-def get_vectorstore(chunks):
-    embeddings=OpenAIEmbeddings()
-    vectorstore=faiss.FAISS.from_texts(texts=chunks,embedding=embeddings)
-    return vectorstore
-
-# generating conversation chain  
-def get_conversationchain(vectorstore):
-    llm=ChatOpenAI(temperature=0.4,model_name='gpt-4o-mini')
-    memory = ConversationBufferMemory(memory_key='chat_history', 
-                                      return_messages=True,
-                                      output_key='answer') # using conversation buffer memory to hold past information
-    conversation_chain = ConversationalRetrievalChain.from_llm(
-                                llm=llm,
-                                retriever=vectorstore.as_retriever(),
-                                condense_question_prompt=CUSTOM_QUESTION_PROMPT,
-                                memory=memory)
-    return conversation_chain
-
-# generating response from user queries and displaying them accordingly
-def handle_question(question):
-    response=st.session_state.conversation({'question': question})
-    st.session_state.chat_history=response["chat_history"]
-    for i,msg in enumerate(st.session_state.chat_history):
-        if i%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_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:")
-    question=st.text_input("Ask question from your document:")
-    if question:
-        handle_question(question)
-    with st.sidebar:
-        st.subheader("Your documents")
-        docs=st.file_uploader("Upload your PDF here and click on 'Process'",accept_multiple_files=True)
-        if st.button("Process"):
-            with st.spinner("Processing"):
-                
-                #get the pdf
-                raw_text=get_pdf_text(docs)
-                
-                #get the text chunks
-                text_chunks=get_chunks(raw_text)
-                
-                #create vectorstore
-                vectorstore=get_vectorstore(text_chunks)
-                
-                #create conversation chain
-                st.session_state.conversation=get_conversationchain(vectorstore)
-
-
-if __name__ == '__main__':
-    main()