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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 RetrievalQA # for conversing with chatGPT
from langchain.chat_models import ChatOpenAI # the LLM model we'll use (ChatGPT)
from langchain import PromptTemplate
def load_doc(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 RetrievalQAChain class
global pdf_qa
prompt_template = """Use the following pieces of context to answer the question at the end. If you do not know the answer, just return the question followed by N/A. If you encounter a date, return it in mm/dd/yyyy format.
{context}
Question: {question}
Return the key fields from the question followed by the answer :"""
PROMPT = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
chain_type_kwargs = {"prompt": PROMPT}
pdf_qa = RetrievalQA.from_chain_type(llm=ChatOpenAI(temperature=0, model_name="gpt-4"),chain_type="stuff", retriever=vectordb.as_retriever(), chain_type_kwargs=chain_type_kwargs, return_source_documents=False)
return "Ready"
else:
return "Please provide an OpenAI API key"
def answer_query(query):
question = query
return pdf_qa.run(question)
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 built on GPT-4</p>
</div>
"""
with gr.Blocks(css=css,theme=gr.themes.Monochrome()) as demo:
gr.HTML(html)
with gr.Column():
openai_key = gr.Textbox(label="Your GPT-4 OpenAI API key", type="password")
pdf_doc = gr.File(label="Load a pdf",file_types=['.pdf'],type='file')
with gr.Row():
status = gr.Textbox(label="Status", placeholder="", interactive=False)
load_pdf = gr.Button("Load PDF to LangChain")
with gr.Row():
input = gr.Textbox(label="Type in your question")
output = gr.Textbox(label="Answer")
submit_query = gr.Button("Submit")
load_pdf.click(load_doc, inputs=[pdf_doc, openai_key], outputs=status)
submit_query.click(answer_query,input,output)
#forcing a save in order to re-build the container.
demo.launch()
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