app.py

from langchain_community.llms import CTransformers
from ctransformers import AutoModelForCausalLM
from langchain.agents import Tool
from langchain.agents import AgentType, initialize_agent
from langchain.chains import RetrievalQA
from langchain.text_splitter import CharacterTextSplitter
from langchain_community.document_loaders import PyPDFLoader
from langchain_community.vectorstores import FAISS
from langchain_community.embeddings import HuggingFaceBgeEmbeddings
import tempfile
import os
import streamlit as st
import timeit
from langchain.callbacks.tracers import ConsoleCallbackHandler

# tt
def main():

    FILE_LOADER_MAPPING = {
    "pdf": (PyPDFLoader, {})
    # Add more mappings for other file extensions and loaders as needed
    }

    st.title("Document Comparison with Q&A using Agents")
    
    

    # Upload files
    uploaded_files = st.file_uploader("Upload your documents", type=["pdf"], accept_multiple_files=True)
    loaded_documents = []

    if uploaded_files:
        # Create a temporary directory
        with tempfile.TemporaryDirectory() as td:
            # Move the uploaded files to the temporary directory and process them
            for uploaded_file in uploaded_files:
                st.write(f"Uploaded: {uploaded_file.name}")
                ext = os.path.splitext(uploaded_file.name)[-1][1:].lower()
                st.write(f"Uploaded: {ext}")

                # Check if the extension is in FILE_LOADER_MAPPING
                if ext in FILE_LOADER_MAPPING:
                    loader_class, loader_args = FILE_LOADER_MAPPING[ext]
                    # st.write(f"loader_class: {loader_class}")

                    # Save the uploaded file to the temporary directory
                    file_path = os.path.join(td, uploaded_file.name)
                    with open(file_path, 'wb') as temp_file:
                        temp_file.write(uploaded_file.read())

                    # Use Langchain loader to process the file
                    loader = loader_class(file_path, **loader_args)
                    loaded_documents.extend(loader.load())
                else:
                    st.warning(f"Unsupported file extension: {ext}, the app currently only supports pdf")

        st.write("Ask question to get comparison from the documents:")
        query = st.text_input("Ask a question:")


    

        if st.button("Get Answer"):
            if query:
                # Load model, set prompts, create vector database, and retrieve answer
                try:
                    start = timeit.default_timer()
                    # config = {
                    # 'max_new_tokens': 1024,
                    # 'repetition_penalty': 1.1,
                    # 'temperature': 0.1,
                    # 'top_k': 50,
                    # 'top_p': 0.9,
                    # 'stream': True,
                    # 'threads': int(os.cpu_count() / 2)
                    # }
                        
                    llm = CTransformers(
                    # model = "TheBloke/Mistral-7B-Instruct-v0.2-GGUF",
                    model= "TheBloke/Llama-2-7B-Chat-GGUF",
                    model_file = "llama-2-7b-chat.Q3_K_S.gguf",
                    model_type="llama",
                    max_new_tokens = 300,
                    temperature = 0.3,
                    lib="avx2", # for CPU
                    )
                    # llm = AutoModelForCausalLM.from_pretrained("second-state/stablelm-2-zephyr-1.6b-GGUF", model_type="stablelm-2-zephyr-1_6b-Q4_0.gguf")
                        
                    print("LLM Initialized...")
                        
                        
                        
                    model_name = "BAAI/bge-large-en"
                    model_kwargs = {'device': 'cpu'}
                    encode_kwargs = {'normalize_embeddings': False}
                    embeddings = HuggingFaceBgeEmbeddings(
                        model_name=model_name,
                        model_kwargs=model_kwargs,
                        encode_kwargs=encode_kwargs
                    )
    
                    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
                    chunked_documents = text_splitter.split_documents(loaded_documents)
                    retriever = FAISS.from_documents(chunked_documents, embeddings).as_retriever()
                    
                    # Wrap retrievers in a Tool
                    tools = []
                    tools.append(
                        Tool(
                            name="Comparison tool",
                            description="useful when you want to answer questions about the uploaded documents",
                            func=RetrievalQA.from_chain_type(llm=llm, retriever=retriever),
                        )
                    )
    
                    agent = initialize_agent(
                    tools=tools,
                    llm=llm,
                    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
                    verbose=True
                    )
    
                    # response = agent.run(query)
    
                    end = timeit.default_timer()
                    st.write("Elapsed time:")
                    st.write(end - start)
    
                    st.write("Bot Response:")
                    # st.write(agent.invoke(query, config={"callbacks":[ConsoleCallbackHandler()]}))
                    st.write(agent.run({"input": query}))
                    # st.write(response)
    
    
                                             
                except Exception as e:
                    st.error(f"An error occurred: {str(e)}")
            else:
                st.warning("Please enter a question.")
                    

if __name__ == "__main__":
    main()