Spaces:
Sleeping
Sleeping
File size: 3,132 Bytes
3a05b97 5dea20f 3a05b97 705f0e4 3a05b97 8fce35a 3a05b97 |
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 |
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
def loading_pdf():
return "Loading..."
def pdf_changes(pdf_doc, repo_id):
loader = OnlinePDFLoader(pdf_doc.name)
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=300, chunk_overlap=0)
texts = text_splitter.split_documents(documents)
embeddings = HuggingFaceHubEmbeddings()
db = Chroma.from_documents(texts, embeddings)
retriever = db.as_retriever()
llm = HuggingFaceHub(repo_id=repo_id, model_kwargs={"temperature":0.1, "max_new_tokens":250})
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>LangChain ChatBot</h1>
<p style="text-align: center;">Upload a PDF, click the "Load PDF to LangChain" button, <br /></p>
<a style="display:inline-block; margin-left: 1em" href="https://www.adople.com"><img src="https://lh6.googleusercontent.com/FQJXx8B6Tbq7SvSE3wvJyXusFZxKcsY92eQaPnZj5pIDdXHVjs10tXXBqWcF0BgC_riSFcje2qUd-XWaiaJByI6dMOkEFdAtpeG7KK8xh7nH8KE3GfSOMrySKPVWXGdEvg=w1280" alt="Adople AI"></a>
</div>
"""
with gr.Blocks(css=css,theme=gr.themes.Soft()) 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")
repo_id = gr.Dropdown(label="LLM", choices=["google/flan-ul2", "OpenAssistant/oasst-sft-1-pythia-12b", "bigscience/bloomz"], value="google/flan-ul2")
with gr.Row():
langchain_status = gr.Textbox(label="Status", placeholder="", interactive=False)
load_pdf = gr.Button("Load 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")
repo_id.change(pdf_changes, inputs=[pdf_doc, repo_id], outputs=[langchain_status], queue=False)
load_pdf.click(pdf_changes, inputs=[pdf_doc, repo_id], outputs=[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() |