Spaces:
Sleeping
Sleeping
File size: 3,140 Bytes
cc93217 |
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 |
import gradio as gr
from langchain.document_loaders import OnlinePDFLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.prompts import PromptTemplate
text_splitter = CharacterTextSplitter(chunk_size=350, chunk_overlap=0)
from langchain.llms import HuggingFaceHub
flan_ul2 = HuggingFaceHub(repo_id="HuggingFaceH4/zephyr-7b-beta", model_kwargs={"temperature":0.1, "max_new_tokens":300})
from langchain.embeddings import HuggingFaceHubEmbeddings
embeddings = HuggingFaceHubEmbeddings()
from langchain.vectorstores import Chroma
from langchain.chains import RetrievalQA
def loading_pdf():
return "Loading..."
def pdf_changes(pdf_doc):
loader = OnlinePDFLoader(pdf_doc.name)
documents = loader.load()
texts = text_splitter.split_documents(documents)
db = Chroma.from_documents(texts, embeddings)
retriever = db.as_retriever()
prompt_template = """You have been given a pdf or pdfs. You must search these pdfs.
If you don't know the answer, just say that you don't know, don't try to make up an answer.
Only answer the question.
{context}
Question: {question}
Answer:"""
PROMPT = PromptTemplate(
template=prompt_template, input_variables=["context", "question"]
)
chain_type_kwargs = {"prompt": PROMPT}
global qa
qa = RetrievalQA.from_chain_type(
llm=flan_ul2,
chain_type="stuff",
retriever=retriever,
return_source_documents=True,
chain_type_kwargs=chain_type_kwargs,
)
return "Ready"
def add_text(history, text):
history = history + [(text, None)]
return history, ""
def bot(history):
response = infer(history[-1][0])
history[-1][1] = response['result']
return history
def infer(question):
query = question
result = qa({"query": query})
return result
css="""
#col-container {max-width: 700px; margin-left: auto; margin-right: auto;}
"""
title = """
<div style="text-align: center;max-width: 700px;">
<h1>Chat with PDF</h1>
<p style="text-align: center;">Upload a .PDF from your computer, click the "Load PDF to LangChain" button, <br />
when everything is ready, you can start asking questions about the pdf ;)</p>
</div>
"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.HTML(title)
with gr.Column():
pdf_doc = gr.File(label="Load a pdf", file_types=['.pdf'], type="file")
with gr.Row():
langchain_status = gr.Textbox(label="Status", placeholder="", interactive=False)
load_pdf = gr.Button("Load pdf to langchain")
chatbot = gr.Chatbot([], elem_id="chatbot").style(height=350)
with gr.Row():
question = gr.Textbox(label="Question", placeholder="Type your question and hit Enter ")
load_pdf.click(loading_pdf, None, langchain_status, queue=False)
load_pdf.click(pdf_changes, pdf_doc, langchain_status, queue=False)
question.submit(add_text, [chatbot, question], [chatbot, question]).then(
bot, chatbot, chatbot
)
demo.launch() |