Spaces:
Runtime error
Runtime error
File size: 4,439 Bytes
c43278c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 |
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.text_splitter import CharacterTextSplitter
from langchain.chains.question_answering import load_qa_chain
from langchain.llms import OpenAI
from dotenv import load_dotenv
import os
import subprocess
# # Load environment variables from .env file
# load_dotenv()
# # Access environment variables
# OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
# SERPAPI_API_KEY = os.getenv("SERPAPI_API_KEY")
subprocess.run(["git", "clone", "https://github.com/TheMITTech/shakespeare"], check=True)
from glob import glob
files = glob("./shakespeare/**/*.html")
import shutil
import os
os.mkdir('./data')
destination_folder = './data/'
for html_file in files:
shutil.move(html_file, destination_folder + html_file.split("/")[-1])
from langchain.document_loaders import BSHTMLLoader, DirectoryLoader
from bs4 import BeautifulSoup
bshtml_dir_loader = DirectoryLoader('./data/', loader_cls=BSHTMLLoader)
data = bshtml_dir_loader.load()
from transformers import AutoTokenizer
bloomz_tokenizer = AutoTokenizer.from_pretrained("bigscience/bloomz-1b7")
from langchain.text_splitter import CharacterTextSplitter
text_splitter = CharacterTextSplitter.from_huggingface_tokenizer(bloomz_tokenizer, chunk_size=100, chunk_overlap=0, separator="\n")
documents = text_splitter.split_documents(data)
from langchain.embeddings import HuggingFaceEmbeddings
embeddings = HuggingFaceEmbeddings()
from langchain.vectorstores import Chroma
persist_directory = "vector_db"
vectordb = Chroma.from_documents(documents=documents, embedding=embeddings, persist_directory=persist_directory)
vectordb.persist()
vectordb = None
vectordb = Chroma(persist_directory=persist_directory, embedding_function=embeddings)
from langchain import HuggingFacePipeline
llm = HuggingFacePipeline.from_model_id(
model_id="bigscience/bloomz-1b7",
task="text-generation",
model_kwargs={"temperature" : 0, "max_length" : 500})
doc_retriever = vectordb.as_retriever()
from langchain.chains import RetrievalQA
shakespeare_qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=doc_retriever)
# from langchain.utilities import SerpAPIWrapper
# search = SerpAPIWrapper()
# from langchain.agents import initialize_agent, Tool
# from langchain.agents import AgentType
# from langchain.tools import BaseTool
# from langchain.llms import OpenAI
# from langchain import LLMMathChain, SerpAPIWrapper
# tools = [
# Tool(
# name = "Shakespeare QA System",
# func=shakespeare_qa.run,
# description="useful for when you need to answer questions about Shakespeare's works. Input should be a fully formed question."
# ),
# Tool(
# name = "SERP API Search",
# func=search.run,
# description="useful for when you need to answer questions about ruff (a python linter). Input should be a fully formed question."
# ),
# ]
# from langchain.memory import ConversationBufferMemory, ReadOnlySharedMemory
# memory = ConversationBufferMemory(memory_key="chat_history")
# readonlymemory = ReadOnlySharedMemory(memory=memory)
# from langchain.agents import ZeroShotAgent, Tool, AgentExecutor
# prefix = """Have a conversation with a human, answering the following questions as best you can. You have access to the following tools:"""
# suffix = """Begin!"
# {chat_history}
# Question: {input}
# {agent_scratchpad}"""
# prompt = ZeroShotAgent.create_prompt(
# tools,
# prefix=prefix,
# suffix=suffix,
# input_variables=["input", "chat_history", "agent_scratchpad"]
# )
# from langchain import OpenAI, LLMChain, PromptTemplate
# llm_chain = LLMChain(llm=llm, prompt=prompt)
# agent = ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True)
# agent_chain = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True, memory=memory)
def make_inference(query):
return(shakespeare_qa.run(input=query))
if __name__ == "__main__":
# make a gradio interface
import gradio as gr
gr.Interface(
make_inference,
[
gr.inputs.Textbox(lines=2, label="Query"),
],
gr.outputs.Textbox(label="Response"),
title="🗣️QuestionMyDoc-Bloomz1b7📄",
description="🗣️QuestionMyDoc-Bloomz1b7📄 is a tool that allows you to ask questions about a document. In this case - Shakespears.",
).launch() |