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| import streamlit as st | |
| import os | |
| from PyPDF2 import PdfReader | |
| from langchain.text_splitter import CharacterTextSplitter | |
| from langchain.embeddings.openai import OpenAIEmbeddings | |
| from langchain.vectorstores import FAISS | |
| from langchain.chains.question_answering import load_qa_chain | |
| from langchain.callbacks import get_openai_callback | |
| from langchain import HuggingFaceHub, LLMChain | |
| from langchain.embeddings import HuggingFaceHubEmbeddings,HuggingFaceInferenceAPIEmbeddings | |
| token = os.environ['HF_TOKEN'] | |
| repo_id = "sentence-transformers/all-mpnet-base-v2" | |
| hf = HuggingFaceHubEmbeddings( | |
| repo_id=repo_id, | |
| task="feature-extraction", | |
| huggingfacehub_api_token= token, | |
| ) | |
| from langchain.embeddings import HuggingFaceInferenceAPIEmbeddings | |
| embeddings = HuggingFaceInferenceAPIEmbeddings( | |
| api_key=token, model_name="sentence-transformers/all-MiniLM-l6-v2" | |
| ) | |
| def main(): | |
| st.set_page_config(page_title="Ask your PDF") | |
| st.header("Ask your PDF 💬") | |
| # upload file | |
| pdf = st.file_uploader("Upload your PDF", type="pdf") | |
| # extract the text | |
| if pdf is not None: | |
| pdf_reader = PdfReader(pdf) | |
| text = "" | |
| for page in pdf_reader.pages: | |
| text += page.extract_text() | |
| # split into chunks | |
| text_splitter = CharacterTextSplitter( | |
| separator="\n", | |
| chunk_size=1000, | |
| chunk_overlap=200, | |
| length_function=len | |
| ) | |
| chunks = text_splitter.split_text(text) | |
| # create embeddings | |
| # embeddings = OpenAIEmbeddings() | |
| # embeddings = query(chunks) | |
| # embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2") | |
| knowledge_base = FAISS.from_texts(chunks, embeddings) | |
| # show user input | |
| user_question = st.text_input("Ask a question about your PDF:") | |
| if user_question: | |
| docs = knowledge_base.similarity_search(user_question) | |
| # llm = OpenAI() | |
| hub_llm = HuggingFaceHub( | |
| repo_id='HuggingFaceH4/zephyr-7b-beta', | |
| model_kwargs={'temperature':0.01,"max_length": 2048,}, | |
| huggingfacehub_api_token=token) | |
| llm = hub_llm | |
| chain = load_qa_chain(llm, chain_type="stuff") | |
| with get_openai_callback() as cb: | |
| response = chain.run(input_documents=docs, question=user_question) | |
| print(cb) | |
| st.write(response) | |
| if __name__ == '__main__': | |
| main() | |