Create app.py

app.py

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+import gradio as gr
+import os
+import time
+import pandas as pd
+import sqlite3
+import ocrmypdf
+
+from langchain.document_loaders import OnlinePDFLoader  # for loading the pdf
+from langchain.embeddings import HuggingFaceEmbeddings  # open source embedding model
+from langchain.text_splitter import CharacterTextSplitter
+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 an OpenAI API key is provided, it will be used for ChatOpenAI (GPT-4)
+    if open_ai_key is not None:
+        os.environ['OPENAI_API_KEY'] = open_ai_key
+    # OCR Conversion - skips conversion of pages that already contain text
+    pdf_doc = ocr_converter(pdf_doc)
+    # Load the pdf file
+    loader = OnlinePDFLoader(pdf_doc)
+    pages = loader.load_and_split()
+    print('pages loaded:', len(pages))
+    
+    # Create an instance of HuggingFaceEmbeddings (open source) for generating embeddings
+    embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
+    
+    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])
+        
+    # If no valid pages are specified, use the entire PDF.
+    if len(pages_to_be_loaded) == 0:
+        pages_to_be_loaded = pages.copy()
+            
+    # Create a vector store using the Chroma class with the open-source embeddings
+    vectordb = Chroma.from_documents(pages_to_be_loaded, embedding=embeddings)
+    
+    # Configure the prompt template for the QA chain
+    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. If there is a Preface section in the document, extract the chapter# and the short description from the Preface. 
+        Chapter numbers are listed to the left in Preface and always start with an alphabet, for example A1-1.
+        {context}
+        Question: {question}
+        Return the answer. Provide the answer in the JSON format and extract the key from the question. Where applicable, break the answer into bullet points. 
+        When the sentences are long, try and break them into sub sections and include all the information and do not skip any information. 
+        If there is an exception to the answer, please do include it in a 'Note:' section. If there are no exceptions to the answer, please skip the 'Note:' section. 
+        Include a 'For additional details refer to' section when the document has more information to offer on the topic being questioned. 
+        If the document has a Preface or 'Table of Contents' section, extract the chapter# and a short description and include the info under the 'For additional details refer to' section. 
+        List only the chapters that contain information or skip this section altogether. Do not use page numbers as chapter numbers as they are different. 
+        If additional information is found in multiple pages within the same chapter, list the chapter only once. 
+        If chapter information cannot be extracted, include any other information that will help the user navigate to the relevant sections of the document. 
+        If the document does not contain a Preface or 'Table of Contents' section, please do not call that out. For example, do not include statements like 
+        the following in the answer - 'The document does not contain a Preface or 'Table of Contents' section'"""
+    )
+
+    PROMPT = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
+    chain_type_kwargs = {"prompt": PROMPT}
+
+    global pdf_qa
+    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"
+
+def create_db_connection():
+    DB_FILE = "./questionset.db"
+    connection = sqlite3.connect(DB_FILE, check_same_thread=False)
+    return connection
+
+def create_sqlite_table(connection):
+    print("*****Entered the create_sqlite_table method*****")
+    cursor = connection.cursor()
+    try:
+        data = f'SELECT * FROM questions'
+        cursor.execute(data)
+        cursor.fetchall()
+    except sqlite3.OperationalError:
+        cursor.execute(
+        '''
+        CREATE TABLE questions (document_type TEXT NOT NULL, questionset_tag TEXT NOT NULL, field TEXT NOT NULL, question TEXT NOT NULL)
+        ''')
+        print("*****questions table has been created******")
+    connection.commit()
+
+def load_master_questionset_into_sqlite(connection):
+    create_sqlite_table(connection)
+    cursor = connection.cursor()
+    masterlist_for_DOT_count = cursor.execute(
+        "Select COUNT(document_type) from questions where document_type=? and questionset_tag=?",
+        ("DOT", "masterlist",)
+    ).fetchone()[0]
+    if masterlist_for_DOT_count == 0:
+        print("DOT masterlist has not yet been loaded, proceeding to load.")
+        fieldListForDOT, queryListForDOT = create_field_and_question_list_for_DOT()
+        fieldListForTransmittalSummary, queryListForTransmittalSummary = create_field_and_question_list_for_Transmittal_Summary()
+        i = 0
+        print("*****Entered the load master question set method*****")
+        while i < len(queryListForDOT):
+            cursor.execute(
+                "INSERT INTO questions(document_type, questionset_tag, field, question) VALUES(?,?,?,?)", 
+                ["DOT", "masterlist", fieldListForDOT[i], queryListForDOT[i]]
+            )
+            i += 1
+        i = 0
+        while i < len(queryListForTransmittalSummary):
+            cursor.execute(
+                "INSERT INTO questions(document_type, questionset_tag, field, question) VALUES(?,?,?,?)", 
+                ["Transmittal Summary", "masterlist", fieldListForTransmittalSummary[i], queryListForTransmittalSummary[i]]
+            )
+            i += 1
+        connection.commit()
+    total_questions = cursor.execute("Select COUNT(document_type) from questions").fetchone()[0]
+    print("*******Total number of questions in the DB:", total_questions)
+
+def create_field_and_question_list_for_DOT():
+    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"
+    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]
+    return fieldList, queryList
+
+def create_field_and_question_list_for_Transmittal_Summary():
+    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"
+    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]
+    return fieldList, queryList
+
+def retrieve_document_type_and_questionsettag_from_sqlite():
+    connection = create_db_connection()
+    load_master_questionset_into_sqlite(connection)
+    cursor = connection.cursor()
+    rows = cursor.execute("SELECT document_type, questionset_tag FROM questions order by document_type, upper(questionset_tag)").fetchall()
+    print("Number of rows retrieved from DB:", len(rows))
+    list_for_dropdown = []
+    for i in rows:
+        entries_in_row = list(i) 
+        concatenated_value = entries_in_row[0] + ":" + entries_in_row[1]
+        if concatenated_value not in list_for_dropdown:
+            list_for_dropdown.append(concatenated_value)
+            print(concatenated_value)
+    print("Number of unique entries found in the DB:", len(list_for_dropdown))
+    connection.close()
+    return gr.Dropdown.update(choices=list_for_dropdown, value=list_for_dropdown[0])
+
+def retrieve_fields_and_questions(dropdownoption):
+    print("dropdownoption is:", dropdownoption)
+    splitwords = dropdownoption.split(":")
+    connection = create_db_connection()
+    cursor = connection.cursor()
+    fields_and_questions = cursor.execute(
+        "SELECT document_type, field, question FROM questions where document_type=? and questionset_tag=?",
+        (splitwords[0], splitwords[1],)
+    ).fetchall()
+    connection.close()
+    return pd.DataFrame(fields_and_questions, columns=["documentType", "field", "question"])
+
+def add_questionset(data, document_type, tag_for_questionset):
+    connection = create_db_connection()
+    create_sqlite_table(connection)
+    cursor = connection.cursor()
+    for index, row in data.iterrows():
+        cursor.execute(
+            "INSERT INTO questions(document_type, questionset_tag, field, question) VALUES(?,?,?,?)", 
+            [document_type, tag_for_questionset, row['field'], row['question']]
+        )   
+    connection.commit()
+    connection.close()
+
+def load_csv_and_store_questionset_into_sqlite(csv_file, document_type, tag_for_questionset):
+    print('document type is:', document_type)
+    print('tag_for_questionset is:', tag_for_questionset)
+    
+    if tag_for_questionset:
+        if document_type:
+            data = pd.read_csv(csv_file.name)
+            add_questionset(data, document_type, tag_for_questionset)
+            responseString = "Task Complete. Uploaded {} fields and the corresponding questions into the Database for {}:{}".format(data.shape[0], document_type, tag_for_questionset)
+            return responseString
+        else:
+            return "Please select the Document Type and provide a name for the Question Set"
+    
+def answer_predefined_questions(document_type_and_questionset):
+    print('chosen document_type_and_questionset:', document_type_and_questionset)
+    option_chosen = document_type_and_questionset.split(":")
+    document_type = option_chosen[0]
+    question_set = option_chosen[1]
+    fields = []
+    questions = []
+    responses = []
+    connection = create_db_connection()
+    cursor = connection.cursor()
+    if document_type is not None and question_set is not None:
+        rows = cursor.execute(
+            "SELECT field, question FROM questions where document_type=? and questionset_tag=?",
+            (document_type, question_set,)
+        ).fetchall()
+        for i in rows:
+            entries_in_row = list(i) 
+            fields.append(entries_in_row[0])
+            questions.append(entries_in_row[1])
+            responses.append(pdf_qa.run(entries_in_row[1]))
+    else:
+        return "Please choose your Document Type:QuestionSet"
+    return pd.DataFrame({"Field": fields, "Question to gpt-4": questions, "Response from gpt-4": responses})
+
+def ocr_converter(input_file):
+    image_pdf = input_file.name
+    ocrmypdf.ocr(image_pdf, image_pdf, redo_ocr=True, language="eng")
+    return image_pdf
+
+def summarize_contents():
+    question = "Generate a short summary of the contents along with no more than 3 leading/example questions. Do not return the response in json format"
+    return pdf_qa.run(question)    
+
+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>AskMoli - Chatbot for PDFs</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 choose to get answers to the pre-defined question set OR ask your own question <br />
+    The app is built on GPT-4 and leverages the magic of 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.Tab("Chatbot"):
+        with gr.Column():
+            open_ai_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():
+                summary = gr.Textbox(label="Summary")
+                summarize_pdf = gr.Button("Have Moli Summarize the Contents").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 AskMoli").style(full_width=False)
+
+            with gr.Row():
+                questionsets = gr.Dropdown(label="Pre-defined Question Sets stored in the DB", choices=[])
+                load_questionsets = gr.Button("Retrieve Pre-defined Question Sets from DB").style(full_width=False)
+                fields_and_questions = gr.Dataframe(label="Fields and Questions in the chosen Question Set")
+                load_fields_and_questions = gr.Button("Retrieve Pre-defined Questions from the DB for the chosen QuestionSet").style(full_width=False)
+                            
+            with gr.Row():
+                answers = gr.Dataframe(label="Answers to Predefined Question set")
+                answers_for_predefined_question_set = gr.Button("Get answers to the chosen pre-defined question set").style(full_width=False)
+              
+    with gr.Tab("OCR Converter"):
+        with gr.Column():
+            image_pdf = gr.File(label="Load the pdf to be converted", file_types=['.pdf'], type='file')
+            
+        with gr.Row():
+            ocr_pdf = gr.File(label="OCR'd pdf", file_types=['.pdf'], type='file', file_count="single")
+            convert_into_ocr = gr.Button("Convert").style(full_width=False)
+                
+    with gr.Tab("Upload Question Set"):
+        with gr.Column():
+            document_types = [
+                "Mortgage 1040 US Individual Tax Returns 8453 Elec Form",
+                "Mortgage 1098",
+                "Mortgage 1099",
+                "Mortgage Abstract",
+                "Mortgage ACH Authorization Form",
+                "Mortgage Advance Fee Agreement",
+                "Mortgage Affidavit",
+                "Mortgage Affidavit of Suspense Funds",
+                "Mortgage Agreement Documents",
+                "Mortgage Sales Contract",
+                "Mortgage Loan Estimate",
+                "Mortgage Alimony Or Child Support",
+                "Mortgage Amended Proof Of Claim",
+                "Mortgage Amortization Schedule",
+                "Mortgage Flood Insurance",
+                "Mortgage Appraisal Report",
+                "Mortgage Appraisal Disclosure",
+                "Mortgage ARM Letter",
+                "Mortgage Arms Length Affidavit",
+                "Mortgage Assignment-Recorded",
+                "Mortgage Assignment-Unrecorded",
+                "Mortgage Assignment of Rent or Lease",
+                "Mortgage Automated Value Model",
+                "Mortgage Award Letters",
+                "Mortgage Bailee Letter",
+                "Mortgage Balloon Disclosure",
+                "Mortgage Bank Statement",
+                "Mortgage Bankruptcy Documents",
+                "Mortgage Bill of Sale",
+                "Mortgage Billing Statement",
+                "Mortgage Birth-Marriage-Death Certificate",
+                "Mortgage Borrower Certification Authorization",
+                "Mortgage Borrower Response Package",
+                "Mortgage Brokers Price Opinion",
+                "Mortgage Business Plan",
+                "Mortgage Buydown Agreement",
+                "Mortgage Bylaws Covenants Conditions Restrictions",
+                "Mortgage Cash for Keys",
+                "Mortgage Certificate of Redemption",
+                "Mortgage Certificate of Sale",
+                "Mortgage Certificate of Title",
+                "Mortgage Certification of Amount Due Payoff Reinstatement",
+                "Mortgage Checks-Regular or Cashiers",
+                "Mortgage Closing Disclosure",
+                "Mortgage Closing Protection Letter",
+                "Mortgage Closing Other",
+                "Mortgage Code Violations",
+                "Mortgage Request for Release",
+                "Mortgage Certificate of Liability Insurance",
+                "Mortgage Commitment Letter",
+                "Mortgage Complaint",
+                "Mortgage Complaint Answer Counter Claim",
+                "Mortgage Conditional Approval Letter",
+                "Mortgage Conditional Commitment",
+                "Mortgage Consent Order",
+                "Mortgage Consolidated Mortgage CEMA",
+                "Mortgage Conveyance Claims",
+                "Mortgage Correction and Revision Agreement",
+                "Mortgage Correspondence",
+                "Mortgage Court Order Settlement Divorce Decree",
+                "Mortgage Credit Report",
+                "Mortgage Customer Signature Authorization",
+                "Mortgage Debt Validation",
+                "Mortgage Deed",
+                "Mortgage Default Notices",
+                "Mortgage Direct Debit Authorization Form",
+                "Mortgage Disclosure Documents",
+                "Mortgage Document Checklist",
+                "Mortgage Document Correction and Fee Due Agreement",
+                "Mortgage Dodd Frank Certification",
+                "Mortgage Drivers License",
+                "Mortgage Request for VOE",
+                "Mortgage Environmental Indemnity Agreement",
+                "Mortgage Equal Credit Opportunity Act Notice",
+                "Mortgage Escrow Agreement",
+                "Mortgage Escrow Analysis Trial Balance Worksheet",
+                "Mortgage Instructions to Escrow Agent",
+                "Mortgage Escrow Letters",
+                "Mortgage Executed Deeds",
+                "Mortgage Fair Lending Notice",
+                "Mortgage Foreclosure Complaint",
+                "Mortgage Foreclosure Judgement",
+                "Mortgage Foreclosure Sale",
+                "Mortgage FHA Neighborhood Watch",
+                "Mortgage Truth-In-Lending Disclosure Statement",
+                "Mortgage Financial Form",
+                "Mortgage Financing Agreement",
+                "Mortgage First Payment Letter",
+                "Mortgage Forced Place Insurance Documents",
+                "Mortgage Foreclosure Documents",
+                "Mortgage Good Faith Estimate",
+                "Mortgage Guaranty",
+                "Mortgage HAMP Certifications",
+                "Mortgage HOA-Condo Covenants and Dues",
+                "Mortgage Exemption Hold Harmless Letter",
+                "Mortgage Home Equity Signature Verification Card",
+                "Mortgage Home Inspection",
+                "Mortgage Property Liability Insurance",
+                "Mortgage Homeowners Insurance Notice",
+                "Mortgage HUD-1 Settlement Statement",
+                "Mortgage Income Other",
+                "Mortgage Indemnity Agreement",
+                "Mortgage Informed Consumer Choice Disclosure Notice",
+                "Mortgage Initial Escrow Account Disclosure Statement",
+                "Mortgage Invoices",
+                "Mortgage Land Lease or Land Trust",
+                "Mortgage Land Title Adjustment",
+                "Mortgage Last Will and Testament",
+                "Mortgage Legal Description",
+                "Mortgage Letters Of Administration",
+                "Mortgage Letters of Testamentary",
+                "Mortgage Listing Agreement",
+                "Mortgage Litigation Guarantee",
+                "Mortgage DIL Closing",
+                "Mortgage Hardship Letter",
+                "Mortgage Hardship Affidavit",
+                "Mortgage Home Affordable Modification Agreement",
+                "Mortgage Profit And Loss",
+                "Mortgage Earnest Money Promissory Note",
+                "Mortgage Rental Agreement",
+                "Mortgage Repayment Plan",
+                "Mortgage Short Sale Miscellaneous",
+                "Mortgage LM - Trial Offer Letter or Plan",
+                "Mortgage Errors and Omissions Agreement",
+                "Mortgage Custom Type 2",
+                "Mortgage Custom Type 1",
+                "Mortgage Loan Agreement",
+                "Mortgage Loan Closing Information Summary",
+                "Mortgage Loan Modification",
+                "Mortgage Loan Summary Report",
+                "Mortgage Lock Confirmation",
+                "Mortgage Loss Drafts",
+                "Mortgage Loss Mitigation",
+                "Mortgage Lost Assignment Affidavit",
+                "Mortgage Mech Lien",
+                "Mortgage Mediation",
+                "Mortgage MI Claim Explanation of Benefits",
+                "Mortgage MI Policy Cancellation Document",
+                "Mortgage MI Repurchase Document",
+                "Mortgage Miscellaneous Lien Release",
+                "Mortgage Mobile Home Documentation",
+                "Mortgage Monthly Activity Report",
+                "Mortgage Deed of Trust-Recorded",
+                "Mortgage PMI Disclosure",
+                "Mortgage Payments",
+                "Mortgage Deed of Trust-Unrecorded",
+                "Mortgage Motion For Relief",
+                "Mortgage Note",
+                "Mortgage Note Affidavit",
+                "Mortgage Note Endorsements",
+                "Mortgage Notice Of Appearance",
+                "Mortgage Notice of Default Filedrecorded",
+                "Mortgage Notice of Final Cure",
+                "Mortgage Notice of Levy",
+                "Mortgage Notice of Payment Change",
+                "Mortgage Notice of Right to Cancel",
+                "Mortgage Notice of Sale",
+                "Mortgage Notice of Second Lien",
+                "Mortgage Notice of Servicing Transfer-Transferee",
+                "Mortgage Notice of Servicing Transfer-Transferor",
+                "Mortgage Notice of Termination",
+                "Mortgage Notice to Quit",
+                "Mortgage Objection to Claim",
+                "Mortgage Processing and Underwriting Doc Set",
+                "Mortgage Objection to Motion for Relief",
+                "Mortgage Affidavit of Occupancy",
+                "Mortgage Occupancy Agreement",
+                "Mortgage Occupancy Termination Agreement",
+                "Mortgage Ombudsman Documents",
+                "Mortgage Owner Affidavit",
+                "Mortgage Ownership and Encumbrances Report",
+                "Mortgage Pay History External",
+                "Mortgage Paystub",
+                "Mortgage Payoff Demand Statement",
+                "Mortgage PMI Certificate",
+                "Mortgage Post Petition Fee Notices",
+                "Mortgage Post Sale Documents",
+                "Mortgage Power of Attorney-Recorded",
+                "Mortgage Power of Attorney-Unrecorded",
+                "Mortgage Closing Instructions",
+                "Mortgage Preliminary Modification",
+                "Mortgage Merged-Privacy Policy Notice-Title Policy - Privacy Policy-1098 Privacy Policy",
+                "Mortgage Probate Court Order",
+                "Mortgage Proof of Claim",
+                "Mortgage Property Legal and Vesting Report",
+                "Mortgage Property Management Agreement",
+                "Mortgage Property Notices",
+                "Mortgage Public Assistance",
+                "Mortgage Record Owner and Lien Certificate",
+                "Mortgage Recorded Satisfaction",
+                "Mortgage Regfore Affidavit Executed",
+                "Mortgage Release of Lis Pendens",
+                "Mortgage REO Bids",
+                "Mortgage REO Other",
+                "Mortgage Form 26-1820 Report and Certificate of Loan Disbursement",
+                "Mortgage Request for Verification of Rent or Mortgage",
+                "Mortgage Request for Waiver of R.E. Tax Escrow Requirements",
+                "Mortgage 1003",
+                "Mortgage RMA Package",
+                "Mortgage Sale Postponement",
+                "Mortgage Sale or Milestone Rescission",
+                "Mortgage Satisfaction of Judgement Tax Mortgage Liens",
+                "Mortgage Security Agreement",
+                "Mortgage Separation Agreement",
+                "Mortgage Servicing Acquisition",
+                "Mortgage Servicing Disclosure Statement",
+                "Mortgage Short Payoffs",
+                "Mortgage Signature-Name Affidavit",
+                "Mortgage Assumption of Mortgage",
+                "Mortgage SCRA Related Documents",
+                "Mortgage Social Security Card or Customer ID",
+                "Mortgage Soft Delete",
+                "Mortgage Flood Hazard Determination Form",
+                "Mortgage Stipulated Agreement",
+                "Mortgage Subordination Agreement",
+                "Mortgage Subordination Request Form",
+                "Mortgage Appointment of Substitute Trustee",
+                "Mortgage Merged-Real Estate Taxes-Tax Bill-Tax Certificate",
+                "Mortgage Tax Certificate",
+                "Mortgage Tax Record Information Sheet",
+                "Mortgage Tax Liens",
+                "Mortgage Tax Search",
+                "Mortgage Third Party Authorization",
+                "Mortgage Title Commitment-Equity or Property Report",
+                "Mortgage Title Policy",
+                "Mortgage Title Policy Endorsement",
+                "Mortgage Title Search",
+                "Mortgage Title Insurance Other",
+                "Mortgage Transfer of Claim",
+                "Mortgage Uniform Underwriting and Transmittal Summary",
+                "Mortgage Trustee Sale Guarantee",
+                "Mortgage UCC-1 Financing Statement",
+                "Mortgage Others",
+                "Mortgage Unknown",
+                "Mortgage Utility Bill",
+                "Mortgage Valuation Orders",
+                "Mortgage Verification Document Set",
+                "Mortgage Verification of Service for Military Home Buyers",
+                "Mortgage W2",
+                "Mortgage W9",
+                "Mortgage Wire Transfer Instructions",
+                "Mortgage Workmens Compensation",
+                "Mortgage Writ of Possession",
+                "Mortgage Cover Page",
+                "Mortgage Barcode Page",
+                "Mortgage Wisconsin Tax Escrow Option Notice",
+                "Mortgage Hazard Insurance Declaration",
+                "Mortgage Flood Insurance Declaration",
+                "Mortgage Quitclaim Deed",
+                "Mortgage Tax Deed",
+                "Mortgage Warranty Deed",
+                "Mortgage ALTA Settlement Statement",
+                "Mortgage Home Inspection Waiver",
+                "Mortgage Insurance Disclosure"
+            ]
+            document_type_for_questionset = gr.Dropdown(choices=document_types, label="Select the Document Type")
+            tag_for_questionset = gr.Textbox(label="Please provide a name for the question set. Ex: rwikd-dot-basic-questionset-20230707.")
+            csv_file = gr.File(label="Load a csv - 2 columns with the headers as field, question", file_types=['.csv'], type='file')
+            
+            with gr.Row():
+                status_for_loading_csv = gr.Textbox(label="Status", placeholder="", interactive=False)
+                load_csv = gr.Button("Upload data into the database").style(full_width=False)
+        
+    load_pdf.click(load_pdf_and_generate_embeddings, inputs=[pdf_doc, open_ai_key, relevant_pages], outputs=status)
+    summarize_pdf.click(summarize_contents, outputs=summary)
+    load_csv.click(load_csv_and_store_questionset_into_sqlite, inputs=[csv_file, document_type_for_questionset, tag_for_questionset], outputs=status_for_loading_csv)
+
+    load_questionsets.click(retrieve_document_type_and_questionsettag_from_sqlite, outputs=questionsets)
+    load_fields_and_questions.click(retrieve_fields_and_questions, questionsets, fields_and_questions)
+    answers_for_predefined_question_set.click(answer_predefined_questions, questionsets, answers)
+
+    convert_into_ocr.click(ocr_converter, image_pdf, ocr_pdf)
+    submit_query.click(answer_query, input, output)
+
+# Use this flavor of demo.launch if you need the app to have an admin page.
+demo.launch(debug=True)