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| import os | |
| import requests | |
| import io | |
| import time | |
| import transformers | |
| from getpass import getpass | |
| from langchain import HuggingFaceHub | |
| from langchain.embeddings import HuggingFaceEmbeddings | |
| from langchain.text_splitter import TokenTextSplitter, CharacterTextSplitter | |
| from langchain import PromptTemplate, LLMChain | |
| from langchain.chains.question_answering import load_qa_chain | |
| from langchain.vectorstores import FAISS | |
| from langchain.document_loaders import TextLoader | |
| from langchain.callbacks.manager import CallbackManager | |
| from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler | |
| import gradio as gr | |
| # example questions | |
| question_examples = [ | |
| "What is the name of the captain of the Nautilus?", | |
| "What are the names of the crew members of the Nautilus?", | |
| "What is the name of the Canadian?", | |
| "What is Ned Land's profession?", | |
| "What musical instrument does Captain Nemo play?", | |
| "What is the name of Professor Aronnax's butler?", | |
| "In which areas was the Nautilus traveling?", | |
| "Why doesn't Captain Nemo hate the society?" | |
| ] | |
| # define the API token | |
| os.environ["HUGGINGFACEHUB_API_TOKEN"] = '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' | |
| # used repository for llm | |
| REPO_ID = "declare-lab/flan-alpaca-large" | |
| transformers.utils.move_cache() | |
| llm = HuggingFaceHub( | |
| repo_id=REPO_ID, | |
| model_kwargs={"temperature":0, "max_length":512} | |
| ) | |
| embeddings = HuggingFaceEmbeddings() | |
| callback_manager = CallbackManager([StreamingStdOutCallbackHandler()]) | |
| def read_textfile(File): | |
| loader = TextLoader(File.name) | |
| documents = loader.load() | |
| text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=10) | |
| docs = text_splitter.split_documents(documents) | |
| db = FAISS.from_documents(docs, embeddings) | |
| db.save_local(folder_path=".", index_name="faiss_index") | |
| def ask_question(question, chat_history): | |
| chain = load_qa_chain(llm, chain_type="stuff", verbose=False) | |
| db = FAISS.load_local(folder_path=".", embeddings=embeddings, index_name="faiss_index") | |
| relevant_docs = db.similarity_search(question) | |
| answer = chain.run( | |
| input_documents=relevant_docs, | |
| question=question, | |
| callbacks=callback_manager | |
| ) | |
| chat_history.append((question, answer)) | |
| time.sleep(1) | |
| return "", chat_history | |
| with gr.Blocks() as demo: | |
| gr.Markdown("## 📚AskYourBooks") | |
| gr.Markdown(""" | |
| Ask questions to your books in txt format. After a short indexing time, | |
| ask questions about the content of the book. | |
| """ | |
| ) | |
| textfile = gr.File(label="Drag your EPUB book here") | |
| ask_textbox = gr.Textbox(label="Question") | |
| sample_questions = gr.Examples(examples=question_examples, inputs=[ask_textbox]) | |
| ask_button = gr.Button("Ask your book") | |
| clear_button = gr.Button("Clear chat") | |
| chatbot = gr.Chatbot(label="Conversation") | |
| textfile.change(fn=read_textfile, inputs=[textfile], outputs=[]) | |
| ask_button.click( | |
| fn=ask_question, | |
| inputs=[ask_textbox, chatbot], | |
| outputs=[ask_textbox, chatbot] | |
| ) | |
| clear_button.click(lambda: None, None, chatbot, queue=False) | |