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import gradio as gr
import os
import time
from langchain.document_loaders import OnlinePDFLoader #for laoding the pdf
from langchain.embeddings import OpenAIEmbeddings # for creating embeddings
from langchain.vectorstores import Chroma # for the vectorization part
from langchain.chains import ConversationalRetrievalChain # for conversing with chatGPT
from langchain.chat_models import ChatOpenAI # the LLM model we'll use (ChatGPT)
def loading_pdf():
return "Loading..."
def pdf_changes(pdf_doc, open_ai_key):
if openai_key is not None:
os.environ['OPENAI_API_KEY'] = open_ai_key
#Load the pdf file
loader = OnlinePDFLoader(pdf_doc.name)
pages = loader.load_and_split()
#Create an instance of OpenAIEmbeddings, which is responsible for generating embeddings for text
embeddings = OpenAIEmbeddings()
#To create a vector store, we use the Chroma class, which takes the documents (pages in our case), the embeddings instance, and a directory to store the vector data
vectordb = Chroma.from_documents(pages, embedding=embeddings)
#Finally, we create the bot using the ConversationalRetrievalChain class
#A ConversationalRetrievalChain is similar to a RetrievalQAChain, except that the ConversationalRetrievalChain allows for
#passing in of a chat history which can be used to allow for follow up questions.
global pdf_qa
pdf_qa = ConversationalRetrievalChain.from_llm(ChatOpenAI(temperature=0, model_name="gpt-4"), vectordb.as_retriever(), return_source_documents=False)
return "Ready"
else:
return "Please provide an OpenAI API key"
def add_text(history, text):
history = history + [(text, None)]
return history, ""
def bot(history):
response = infer(history[-1][0], history)
history[-1][1] = ""
for character in response:
history[-1][1] += character
time.sleep(0.05)
yield history
def infer(question, history):
results = []
for human, ai in history[:-1]:
pair = (human, ai)
results.append(pair)
chat_history = results
print(chat_history)
query = question
result = pdf_qa({"question": query, "chat_history": chat_history})
print(result)
return result["answer"]
css="""
#col-container {max-width: 700px; margin-left: auto; margin-right: auto;}
"""
title = """
<div style="text-align: center;max-width: 700px;">
<h1>Chatbot for PDFs - GPT-4</h1>
<p style="text-align: center;">Upload a .PDF, click the "Load PDF to LangChain" button, <br />
Wait for the Status to show Ready, start typing your questions. <br />
The app is set to store chat-history and is built on GPT-4</p>
</div>
"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.HTML(title)
with gr.Column():
openai_key = gr.Textbox(label="Your OpenAI API key", type="password")
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, inputs=[pdf_doc, openai_key], 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() |