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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_chaings(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 = """
<h1>Chat with PDF</h1>
"""
with gr.Blocks(css=css, theme='Taithrah/Minimal') as demo:
with gr.Column(elem_id='col-container'):
gr.HTML(title)
with gr.Column():
pdf_doc = gr.File(label='Upload a PDF', file_types=['.pdf'])
repo_id = gr.Dropdown(label='LLM',
choices=[
'mistralai/Mistral-7B-Instruct-v0.1',
'HuggingFaceH4/zephyr-7b-beta',
'meta-llama/Llama-2-7b-chat-hf',
'01-ai/Yi-6B-200K'
],
value='mistralai/Mistral-7B-Instruct-v0.1')
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 query')
submit_btn = gr.Button('Send')
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(max_size=32) |