Delete app.py

app.py

@@ -1,130 +0,0 @@
-import os
-import logging
-import time
-from dotenv import load_dotenv
-import streamlit as st
-from PyPDF2 import PdfReader
-from langchain.text_splitter import CharacterTextSplitter
-from langchain_cohere import CohereEmbeddings
-from langchain.vectorstores import FAISS
-from langchain.memory import ConversationBufferMemory
-from langchain.chains import ConversationalRetrievalChain
-from langchain_groq import ChatGroq
-
-# Load environment variables
-load_dotenv()
-
-# Set up logging
-logging.basicConfig(
-    level=logging.INFO,
-    format="%(asctime)s - %(levelname)s - %(message)s"
-)
-
-# Function to extract text from PDF files
-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
-
-# Function to split the extracted text into chunks
-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
-
-# Function to create a FAISS vectorstore with rate-limiting and retry logic
-def get_vectorstore(text_chunks):
-    cohere_api_key = os.getenv("COHERE_API_KEY")
-    embeddings = CohereEmbeddings(model="embed-english-v3.0", cohere_api_key=cohere_api_key)
-    
-    vectorstore = None
-    batch_size = 10  # Process chunks in batches of 10
-    for i in range(0, len(text_chunks), batch_size):
-        batch = text_chunks[i:i+batch_size]
-        retry_count = 0
-        
-        while retry_count < 5:  # Retry up to 5 times
-            try:
-                if vectorstore is None:
-                    vectorstore = FAISS.from_texts(texts=batch, embedding=embeddings)
-                else:
-                    vectorstore.add_texts(batch, embedding=embeddings)
-                break  # Exit retry loop if successful
-            except Exception as e:
-                if "rate limit" in str(e).lower():
-                    logging.warning(f"Rate limit exceeded. Retrying batch {i//batch_size + 1} in {2 ** retry_count} seconds...")
-                    time.sleep(2 ** retry_count)  # Exponential backoff
-                    retry_count += 1
-                else:
-                    raise e  # Raise other errors
-    return vectorstore
-
-# Function to set up the conversational retrieval chain
-def get_conversation_chain(vectorstore):
-    try:
-        llm = ChatGroq(model="llama-3.1-70b-versatile", temperature=0.5)
-        memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
-        
-        conversation_chain = ConversationalRetrievalChain.from_llm(
-            llm=llm,
-            retriever=vectorstore.as_retriever(),
-            memory=memory
-        )
-        
-        logging.info("Conversation chain created successfully.")
-        return conversation_chain
-    except Exception as e:
-        logging.error(f"Error creating conversation chain: {e}")
-        st.error("An error occurred while setting up the conversation chain.")
-
-# Handle user input
-def handle_userinput(user_question):
-    if st.session_state.conversation is not None:
-        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(f"*User:* {message.content}")
-            else:
-                st.write(f"*Bot:* {message.content}")
-    else:
-        st.warning("Please process the documents first.")
-
-# Main function to run the Streamlit app
-def main():
-    load_dotenv()
-    st.set_page_config(page_title="Chat with multiple PDFs", page_icon=":books:")
-
-    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..."):
-                raw_text = get_pdf_text(pdf_docs)
-                text_chunks = get_text_chunks(raw_text)
-                vectorstore = get_vectorstore(text_chunks)
-                st.session_state.conversation = get_conversation_chain(vectorstore)
-
-if __name__ == "__main__":
-    main()