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import streamlit as st |
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from dotenv import load_dotenv |
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from langchain.text_splitter import CharacterTextSplitter, RecursiveCharacterTextSplitter |
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from langchain.vectorstores import FAISS |
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from langchain.embeddings import HuggingFaceEmbeddings |
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from langchain.memory import ConversationBufferMemory |
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from langchain.chains import ConversationalRetrievalChain |
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from htmlTemplates import css, bot_template, user_template |
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from langchain.llms import LlamaCpp |
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from langchain.document_loaders import PyPDFLoader, TextLoader, JSONLoader, CSVLoader |
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import json |
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from pathlib import Path |
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from pprint import pprint |
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import tempfile |
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import os |
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from huggingface_hub import hf_hub_download |
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def get_pdf_text(pdf_docs): |
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temp_dir = tempfile.TemporaryDirectory() |
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temp_filepath = os.path.join(temp_dir.name, pdf_docs.name) |
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with open(temp_filepath, "wb") as f: |
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f.write(pdf_docs.getvalue()) |
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pdf_loader = PyPDFLoader(temp_filepath) |
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pdf_doc = pdf_loader.load() |
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return pdf_doc |
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def get_text_file(txt_docs): |
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temp_dir = tempfile.TemporaryDirectory() |
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temp_filepath = os.path.join(temp_dir.name, txt_docs.name) |
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with open(temp_filepath, "wb") as f: |
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f.write(txt_docs.getvalue()) |
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txt_loader = TextLoader(temp_filepath) |
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txt_doc = txt_loader.load() |
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return txt_doc |
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def get_csv_file(csv_docs): |
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temp_dir = tempfile.TemporaryDirectory() |
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temp_filepath = os.path.join(temp_dir.name, csv_docs.name) |
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with open(temp_filepath, "wb") as f: |
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f.write(csv_docs.getvalue()) |
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csv_loader = CSVLoader(temp_filepath) |
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csv_doc = pdf_loader.load() |
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return csv_doc |
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def get_json_file(json_docs): |
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temp_dir = tempfile.TemporaryDirectory() |
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temp_filepath = os.path.join(temp_dir.name, json_docs.name) |
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with open(temp_filepath, "wb") as f: |
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f.write(json_docs.getvalue()) |
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json_loader = JSONLoader( |
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file_path='./example_data/facebook_chat.json', |
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jq_schema='.messages[].content', |
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text_content=False) |
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json_doc = json_loader.load() |
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return json_doc |
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def get_text_chunks(documents): |
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text_splitter = RecursiveCharacterTextSplitter( |
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chunk_size=1000, |
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chunk_overlap=200, |
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length_function=len |
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) |
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documents = text_splitter.split_documents(documents) |
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return documents |
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def get_vectorstore(text_chunks): |
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embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L12-v2', |
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model_kwargs={'device': 'cpu'}) |
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vectorstore = FAISS.from_documents(text_chunks, embeddings) |
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return vectorstore |
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def get_conversation_chain(vectorstore): |
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model_name_or_path = 'TheBloke/Llama-2-7B-chat-GGUF' |
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model_basename = 'llama-2-7b-chat.Q2_K.gguf' |
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model_path = hf_hub_download(repo_id=model_name_or_path, filename=model_basename) |
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llm = LlamaCpp(model_path=model_path, |
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n_ctx=4086, |
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input={"temperature": 0.75, "max_length": 2000, "top_p": 1}, |
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verbose=True, ) |
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memory = ConversationBufferMemory( |
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memory_key='chat_history', return_messages=True) |
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conversation_chain = ConversationalRetrievalChain.from_llm( |
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llm=llm, |
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retriever=vectorstore.as_retriever(), |
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memory=memory |
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) |
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return conversation_chain |
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def handle_userinput(user_question): |
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print('user_question => ', user_question) |
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response = st.session_state.conversation({'question': user_question}) |
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st.session_state.chat_history = response['chat_history'] |
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for i, message in enumerate(st.session_state.chat_history): |
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if i % 2 == 0: |
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st.write(user_template.replace( |
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"{{MSG}}", message.content), unsafe_allow_html=True) |
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else: |
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st.write(bot_template.replace( |
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"{{MSG}}", message.content), unsafe_allow_html=True) |
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def main(): |
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load_dotenv() |
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st.set_page_config(page_title="Chat with multiple Files", |
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page_icon=":books:") |
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st.write(css, unsafe_allow_html=True) |
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if "conversation" not in st.session_state: |
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st.session_state.conversation = None |
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if "chat_history" not in st.session_state: |
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st.session_state.chat_history = None |
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st.header("Chat with multiple Files:") |
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user_question = st.text_input("Ask a question about your documents:") |
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if user_question: |
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handle_userinput(user_question) |
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with st.sidebar: |
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st.subheader("Your documents") |
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docs = st.file_uploader( |
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"Upload your PDFs here and click on 'Process'", accept_multiple_files=True) |
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if st.button("Process"): |
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with st.spinner("Processing"): |
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doc_list = [] |
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for file in docs: |
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print('file - type : ', file.type) |
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if file.type == 'text/plain': |
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doc_list.extend(get_text_file(file)) |
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elif file.type in ['application/octet-stream', 'application/pdf']: |
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doc_list.extend(get_pdf_text(file)) |
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elif file.type == 'text/csv': |
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doc_list.extend(get_csv_file(file)) |
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elif file.type == 'application/json': |
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doc_list.extend(get_json_file(file)) |
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text_chunks = get_text_chunks(doc_list) |
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vectorstore = get_vectorstore(text_chunks) |
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st.session_state.conversation = get_conversation_chain( |
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vectorstore) |
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if __name__ == '__main__': |
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main() |