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""" |
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creator: Lewis Kamau Kimaru |
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Function: chat with pdf documents in different languages |
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""" |
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from langchain.text_splitter import CharacterTextSplitter |
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from langchain.embeddings import HuggingFaceBgeEmbeddings |
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from langchain.vectorstores import FAISS |
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from langchain.chat_models import ChatOpenAI |
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from langchain.memory import ConversationBufferMemory |
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from langchain.chains import ConversationalRetrievalChain |
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from langchain.llms import HuggingFaceHub |
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from typing import Union |
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from dotenv import load_dotenv |
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from PyPDF2 import PdfReader |
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import streamlit as st |
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import requests |
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import json |
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import os |
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os.environ["HUGGINGFACEHUB_API_TOKEN"] = st.secrets['huggingface_token'] |
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st.set_page_config(page_title="SemaNaPDF", page_icon="📚",) |
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Public_Url = 'https://jikoni-tmodel.hf.space' |
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def translate(userinput, target_lang, source_lang=None): |
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if source_lang: |
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url = f"{Public_Url}/translate_enter/" |
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data = { |
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"userinput": userinput, |
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"source_lang": source_lang, |
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"target_lang": target_lang, |
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} |
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response = requests.post(url, json=data) |
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result = response.json() |
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print(type(result)) |
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source_lange = source_lang |
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translation = result['translated_text'] |
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else: |
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url = f"{Public_Url}/translate_detect/" |
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data = { |
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"userinput": userinput, |
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"target_lang": target_lang, |
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} |
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response = requests.post(url, json=data) |
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result = response.json() |
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source_lange = result['source_language'] |
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translation = result['translated_text'] |
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return source_lange, translation |
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def get_pdf_text(pdf : Union[str, bytes, bytearray]) -> str: |
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reader = PdfReader(pdf) |
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pdf_text = '' |
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for page in (reader.pages): |
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text = page.extract_text() |
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if text: |
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pdf_text += text |
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return text |
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def get_text_chunks(text:str) ->list: |
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text_splitter = CharacterTextSplitter( |
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separator="\n", chunk_size=1500, chunk_overlap=300, length_function=len |
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) |
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chunks = text_splitter.split_text(text) |
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return chunks |
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def get_vectorstore(text_chunks : list) -> FAISS: |
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model = "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2" |
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encode_kwargs = { |
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"normalize_embeddings": True |
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} |
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embeddings = HuggingFaceBgeEmbeddings( |
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model_name=model, encode_kwargs=encode_kwargs, model_kwargs={"device": "cpu"} |
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) |
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vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings) |
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return vectorstore |
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def get_conversation_chain(vectorstore:FAISS) -> ConversationalRetrievalChain: |
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llm = HuggingFaceHub( |
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repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1", |
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model_kwargs={"temperature": 0.5, "max_length": 1048}, |
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) |
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memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True) |
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conversation_chain = ConversationalRetrievalChain.from_llm( |
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llm=llm, retriever=vectorstore.as_retriever(), memory=memory |
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) |
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return conversation_chain |
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st.markdown (""" |
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<style> div.stSpinner > div { |
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text-align:center; |
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text-align:center; |
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align-items: center; |
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justify-content: center; |
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} |
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</style>""", unsafe_allow_html=True) |
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footer = """ |
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<style> |
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.footer { |
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position: fixed; |
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bottom: 0; |
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right: 0; |
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background-color: #f8f9fa; |
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padding: 10px; |
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text-align: right; |
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width: 100%; |
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} |
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</style> |
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<div class="footer"> |
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©2023 Lewis Kimaru. All rights reserved. |
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</div> |
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""" |
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def main(): |
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st.title("SemaNaPDF📚") |
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pdf = st.file_uploader("Upload a PDF Document", type="pdf") |
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if pdf is not None: |
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with st.spinner(""): |
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raw_text = get_pdf_text(pdf) |
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text_chunks = get_text_chunks(raw_text) |
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vectorstore = get_vectorstore(text_chunks) |
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st.session_state.conversation = get_conversation_chain(vectorstore) |
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st.info("done") |
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if "messages" not in st.session_state: |
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st.session_state.messages = [] |
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for message in st.session_state.messages: |
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with st.chat_message(message["role"]): |
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st.markdown(message["content"]) |
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if user_question := st.chat_input("Ask your document anything ......?"): |
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with st.chat_message("user"): |
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st.markdown(user_question) |
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user_langd, Queryd = translate(user_question, 'eng_Latn') |
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st.session_state.messages.append({"role": "user", "content": user_question}) |
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response = st.session_state.conversation({"question": Queryd}) |
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print(response) |
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st.session_state.chat_history = response["chat_history"] |
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output = translate(response['answer'], user_langd, 'eng_Latn')[1] |
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with st.chat_message("assistant"): |
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st.markdown(output) |
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st.session_state.messages.append({"role": "assistant", "content": response['answer']}) |
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st.markdown(footer, unsafe_allow_html=True) |
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if __name__ == '__main__': |
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main() |
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