app.py

import gradio as gr
import os
import time
import pandas as pd


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_pdf_and_generate_embeddings(pdf_doc, open_ai_key, relevant_pages):
    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()

        pages_to_be_loaded =[]

        if relevant_pages:
            page_numbers = relevant_pages.split(",")
            if len(page_numbers) != 0:
                for page_number in page_numbers:
                    if page_number.isdigit():
                        pageIndex = int(page_number)-1
                        if pageIndex >=0 and pageIndex <len(pages):
                            pages_to_be_loaded.append(pages[pageIndex])
        #In the scenario where none of the page numbers supplied exist in the PDF, we will revert to using the entire PDF.
        if len(pages_to_be_loaded) ==0:
            pages_to_be_loaded = pages.copy()
            
             
        #To create a vector store, we use the Chroma class, which takes the documents (pages in our case) and the embeddings instance
        vectordb = Chroma.from_documents(pages_to_be_loaded, embedding=embeddings)
        
        #Finally, we create the bot using the RetrievalQA 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 N/A. If you encounter a date, return it in mm/dd/yyyy format.

        {context}

        Question: {question}
        Return just 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(search_kwargs={"k": 5}), chain_type_kwargs=chain_type_kwargs, return_source_documents=False)
                
        return "Ready"
    else:
        return "Please provide an OpenAI gpt-4 API key"
        

def answer_predefined_questions(document_type):
    
    if document_type == "Deed of Trust":
        #Create a list of questions around the relevant fields of a Deed of Trust(DOT) document
        query1 = "what is the Loan Number?"
        field1 = "Loan Number"
        query2 = "Who is the Borrower?"
        field2 = "Borrower"
        query3 = "what is the Case Number?"
        field3 = "Case Number"
        query4 = "what is the Mortgage Identification number?"
        field4 = "MIN Number"
        query5 = "DOT signed date?"
        field5 = "Signed Date"
        query6 = "Who is the Lender?"
        field6 = "Lender"
        query7 = "what is the VA/FHA Number?"
        field7 = "VA/FHA Number"
        query8 = "Who is the Co-Borrower?"
        field8 = "Co-Borrower"
        query9 = "What is the property type - single family, multi family?"
        field9 = "Property Type"
        query10 = "what is the Property Address?"
        field10 = "Property Address"
        query11 = "In what County is the property located?"
        field11 = "Property County"
        query12 = "what is the Electronically recorded date"
        field12 = "Electronic Recording Date"

    
        
    elif document_type == "Transmittal Summary":
        #Create a list of questions around the relevant fields of a TRANSMITTAL SUMMARY document
        query1 = "Who is the Borrower?"
        field1 = "Borrower"
        query2 = "what is the Property Address?"
        field2 = "Property Address"
        query3 = "what is the Loan Term?"
        field3 = "Loan Term"
        query4 = "What is the Base Income?"
        field4 = "Base Income"
        query5 = "what is the Borrower's SSN?"
        field5 = "Borrower's SSN"
        query6 = "Who is the Co-Borrower?"
        field6 = "Co-Borrower"
        query7 = "What is the Original Loan Amount?"
        field7 = "Original Loan Amount"
        query8 = "What is the Initial P&I payment?"
        field8 = "Initial P&I payment"
        query9 = "What is the Co-Borrower's SSN?"
        field9 = "Co-Borrower’s SSN"
        query10 = "Number of units?"
        field10 = "Units#"
        query11 = "Who is the Seller?"
        field11 = "Seller"
        query12 = "Document signed date?"
        field12 = "Signed Date"



    else:
        return "Please choose your Document Type"

    queryList = [query1, query2, query3, query4, query5, query6, query7, query8, query9, query10, query11,query12]
    fieldList = [field1, field2, field3, field4, field5, field6, field7, field8, field9, field10, field11,field12]
    responseList =[]

    i = 0
    while i < len(queryList):
        question = queryList[i]
        responseList.append(pdf_qa.run(question))
        i = i+1

    return pd.DataFrame({"Field": [fieldList[0],fieldList[1],fieldList[2],fieldList[3],fieldList[4],fieldList[5],fieldList[6],fieldList[7],fieldList[8],fieldList[9],fieldList[10],fieldList[11]], 
                         "Question to gpt-4": [queryList[0],queryList[1],queryList[2],queryList[3],queryList[4],queryList[5],queryList[6],queryList[7],queryList[8],queryList[9],queryList[10],queryList[11]], 
                         "Response from gpt-4": [responseList[0],responseList[1],responseList[2],responseList[3],responseList[4],responseList[5],responseList[6],responseList[7],responseList[8],responseList[9],responseList[10],responseList[11]]})
        

    
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 "Upload PDF and generate embeddings" button, <br />
    Wait for the Status to show Ready. You can chose to get answers to the pre-defined question set OR ask your own question <br />
    The app is built on GPT-4 and leverages PromptTemplate</p>
</div>
"""

with gr.Blocks(css=css,theme=gr.themes.Monochrome()) as demo:
    with gr.Column(elem_id="col-container"):
        gr.HTML(title)
    
    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')
        relevant_pages = gr.Textbox(label="*Optional - List comma separated page numbers to load or leave this field blank to use the entire PDF")
        
        with gr.Row():
            status = gr.Textbox(label="Status", placeholder="", interactive=False)
            load_pdf = gr.Button("Upload PDF and generate embeddings").style(full_width=False)
            
        with gr.Row():
            document_type = gr.Radio(['Deed of Trust', 'Transmittal Summary'], label="Select the Document Type")
            answers = gr.Dataframe(label="Answers to Predefined Question set")
            answers_for_predefined_question_set = gr.Button("Get gpt-4 answers to pre-defined question set").style(full_width=False)
              
        with gr.Row():
            input = gr.Textbox(label="Type in your question")
            output = gr.Textbox(label="Answer")
            submit_query = gr.Button("Submit your own question to gpt-4").style(full_width=False)
        
 
    load_pdf.click(load_pdf_and_generate_embeddings, inputs=[pdf_doc, openai_key, relevant_pages], outputs=status)

    answers_for_predefined_question_set.click(answer_predefined_questions, document_type, answers)
        
    submit_query.click(answer_query,input,output)


demo.launch()