Spaces:
Sleeping
Sleeping
File size: 2,805 Bytes
e348efe 310367e 3404f46 310367e 9814f59 310367e 7432eb1 e348efe 310367e e348efe 310367e e348efe 1fed219 310367e 9814f59 c96ea95 01127eb a03faf2 c96ea95 9814f59 e348efe addace4 e844d1b addace4 90e7fa2 addace4 e844d1b 90e7fa2 e844d1b 90e7fa2 3404f46 90e7fa2 e844d1b 0cc73a7 3404f46 4516329 b3d631d c96ea95 0cc73a7 9814f59 |
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 |
import gradio as gr
from langchain.document_loaders import OnlinePDFLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.llms import HuggingFaceHub
from langchain.embeddings import HuggingFaceHubEmbeddings
from langchain.vectorstores import Chroma
from langchain.chains import RetrievalQA
global llm
def define_llm_model(repo_id):
llm = HuggingFaceHub(repo_id=repo_id, model_kwargs={"temperature":0.1, "max_new_tokens":300})
return "LLM model loaded"
define_llm_model("google/flan-ul2")
def loading_pdf():
return "Loading..."
def pdf_changes(pdf_doc):
loader = OnlinePDFLoader(pdf_doc.name)
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(documents)
embeddings = HuggingFaceHubEmbeddings()
db = Chroma.from_documents(texts, embeddings)
retriever = db.as_retriever()
global qa
qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever, return_source_documents=True)
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)
question = gr.Textbox(label="Question", placeholder="Type your question and hit Enter ")
submit_btn = gr.Button("Send message")
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
)
submit_btn.click(add_text, [chatbot, question], [chatbot, question]).then(
bot, chatbot, chatbot
)
demo.launch() |