first version

app.py

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+import os
+import requests
+import io
+import time
+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
+
+
+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?"
+]
+
+os.environ["HUGGINGFACEHUB_API_TOKEN"]
+
+REPO_ID = "declare-lab/flan-alpaca-large"
+
+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)
+

requirements.txt

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+gradio
+langchain
+transformers
+sentence_transformers
+tiktoken
+faiss-cpu