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# Import necessary libraries
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
# Define a function to display "Loading..." when loading a PDF
def loading_pdf():
return "Loading..."
# Define a function to process PDF changes
def pdf_changes(pdf_doc, repo_id):
# Initialize the OnlinePDFLoader to load the PDF document
loader = OnlinePDFLoader(pdf_doc.name)
documents = loader.load()
# Split the loaded documents into chunks using CharacterTextSplitter
text_splitter = CharacterTextSplitter(chunk_size=400, chunk_overlap=50)
texts = text_splitter.split_documents(documents)
# Initialize HuggingFaceHubEmbeddings for embeddings
embeddings = HuggingFaceHubEmbeddings()
# Create a Chroma vector store from the text chunks and embeddings
db = Chroma.from_documents(texts, embeddings)
# Convert the vector store to a retriever
retriever = db.as_retriever()
# Initialize an HuggingFaceHub language model (LLM)
llm = HuggingFaceHub(repo_id=repo_id, model_kwargs={"temperature": 0.25, "max_new_tokens": 1000})
# Create a RetrievalQA chain with the LLM, retriever, and return_source_documents option
global qa
qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever, return_source_documents=False)
return "Ready"
# Define a function to add text to a history
def add_text(history, text):
history = history + [(text, None)]
return history, ""
# Define a bot function to generate responses
def bot(history):
response = infer(history[-1][0])
history[-1][1] = response['result']
return history
# Define an inference function to query the LLM
def infer(query):
result = qa({"query": query})
return result
# Define custom CSS styles
css = """
#col-container {max-width: 700px; margin-left: auto; margin-right: auto;}
"""
# Define a title HTML for the interface
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>
"""
# Create the Gradio interface
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.HTML(title)
with gr.Column():
# Create a file input for loading PDF
pdf_doc = gr.File(label="Load a PDF", file_types=['.pdf'], type="file", value="AhmedS_Resume.pdf")
# Create a dropdown for selecting the LLM
repo_id = gr.Dropdown(label="LLM", choices=["HuggingFaceH4/zephyr-7b-alpha", "google/flan-t5-xxl", "gpt2-xl"], value="HuggingFaceH4/zephyr-7b-alpha")
with gr.Row():
langchain_status = gr.Textbox(label="Status", placeholder="Waiting...", interactive=False)
load_pdf = gr.Button("Load PDF to LangChain")
chatbot = gr.Chatbot([], elem_id="chatbot").style(height=350)
query = gr.Textbox(label="Question", placeholder="Type your question and hit Enter ")
submit_btn = gr.Button("Send message")
# Set up actions for UI elements
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)
query.submit(add_text, [chatbot, query], [chatbot, query]).then(bot, chatbot, chatbot)
submit_btn.click(add_text, [chatbot, query], [chatbot, query]).then(bot, chatbot, chatbot)
# Launch the Gradio interface
demo.launch()
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