app.py

import gradio as gr
import pandas as pd
from io import BytesIO
import os
import json
from datetime import datetime
import firebase_admin
from firebase_admin import credentials, firestore
from dar_processor import preprocess_pdf_text
from gemini_utils import get_structured_data_with_gemini, get_harmonised_titles
from models import ParsedDARReport, HarmonisedPara

# Firebase setup
FIREBASE_CREDENTIALS = os.environ.get("FIREBASE_CREDENTIALS")
if FIREBASE_CREDENTIALS:
    # Load credentials from environment variable (preferred for security)
    cred = credentials.Certificate(json.loads(FIREBASE_CREDENTIALS))
else:
    # Fallback to reading from firebase.json file
    if not os.path.exists("firebase.json"):
        raise ValueError("firebase.json not found and FIREBASE_CREDENTIALS not set.")
    cred = credentials.Certificate("firebase.json")

firebase_admin.initialize_app(cred)
db = firestore.client()
request_counts = db.collection('request_counts')

def get_request_count():
    """Retrieve the current request count for today."""
    today = datetime.utcnow().strftime('%Y-%m-%d')
    doc_ref = request_counts.document(today)
    doc = doc_ref.get()
    count = doc.to_dict().get('count', 0) if doc.exists else 0
    return count

def check_request_limit():
    """Check if the request limit for the day has been reached."""
    today = datetime.utcnow().strftime('%Y-%m-%d')
    doc_ref = request_counts.document(today)
    doc = doc_ref.get()

    if not doc.exists:
        # Initialize counter for the new day
        doc_ref.set({'count': 0})
        count = 0
    else:
        count = doc.to_dict().get('count', 0)

    if count >= 400:
        return False, "Daily request limit of 400 reached. Try again tomorrow."

    # Increment the counter
    doc_ref.update({'count': firestore.Increment(1)})
    return True, None

def create_html_report(results_with_harmonised: list[dict]) -> str:
    """Generates an HTML string to display the results in a styled table."""
    if not results_with_harmonised:
        return "<p>No audit paras found or processed.</p>"
    style = """
    <style>
        body { font-family: sans-serif; }
        .styled-table {
            border-collapse: collapse; margin: 25px 0; font-size: 0.9em;
            min-width: 400px; box-shadow: 0 0 20px rgba(0, 0, 0, 0.15);
            border-radius: 8px; overflow: hidden;
        }
        .styled-table thead tr { background-color: #009879; color: #ffffff; text-align: left; }
        .styled-table th, .styled-table td { padding: 12px 15px; border-bottom: 1px solid #dddddd; }
        .styled-table tbody tr:last-of-type { border-bottom: 2px solid #009879; }
    </style>
    """
    html = f"{style}<table class='styled-table'><thead><tr><th>Para No.</th><th>Original Audit Para Heading</th><th>Harmonised Audit Para Heading</th><th>Amount Involved (in Lakhs)</th></tr></thead><tbody>"
    for item in results_with_harmonised:
        para_num = item.get('audit_para_number', 'N/A')
        original_heading = item.get('audit_para_heading', 'N/A')
        harmonised_heading = item.get('harmonised_audit_para_heading', 'N/A')
        amount = f"₹{item.get('revenue_involved_lakhs_rs', 0.0):,.2f} L"
        html += f"<tr><td>{para_num}</td><td>{original_heading}</td><td>{harmonised_heading}</td><td>{amount}</td></tr>"
    html += "</tbody></table>"
    return html

def process_dar_pdf(pdf_file):
    """The main processing function, called after successful login."""
    # Check request limit before processing
    can_process, error_msg = check_request_limit()
    if not can_process:
        return error_msg, None, None, f"Requests today: {get_request_count()}/400"

    gemini_api_key = os.environ.get("GEMINI_API_KEY")
    if not pdf_file:
        return "Please upload a PDF file.", None, None, f"Requests today: {get_request_count()}/400"
    if not gemini_api_key:
        return "Error: GEMINI_API_KEY secret not found in Space settings.", None, None, f"Requests today: {get_request_count()}/400"

    # Step 1: Process PDF to text
    full_text = preprocess_pdf_text(pdf_file.name)
    if full_text.startswith("Error"):
        return f"Failed to process PDF: {full_text}", None, None, f"Requests today: {get_request_count()}/400"

    # Step 2: Extract structured data
    parsed_report = get_structured_data_with_gemini(gemini_api_key, full_text)
    if parsed_report.parsing_errors or not parsed_report.audit_paras:
        error_msg = parsed_report.parsing_errors or "Could not find any audit paras."
        return error_msg, None, None, f"Requests today: {get_request_count()}/400"

    # Step 3: Get harmonised titles
    original_headings = [p.audit_para_heading for p in parsed_report.audit_paras if p.audit_para_heading]
    if not original_headings:
        return "Found paras but no headings to harmonise.", None, None, f"Requests today: {get_request_count()}/400"

    harmonised_results = get_harmonised_titles(gemini_api_key, full_text, original_headings)
    if not harmonised_results:
        return "Failed to generate harmonised titles.", None, None, f"Requests today: {get_request_count()}/400"

    # Step 4: Combine and prepare outputs
    harmonised_map = {item.original_heading: item.harmonised_heading for item in harmonised_results}
    final_data_list = []
    for para in parsed_report.audit_paras:
        combined_info = (parsed_report.header.dict() if parsed_report.header else {}) | para.dict()
        combined_info['harmonised_audit_para_heading'] = harmonised_map.get(para.audit_para_heading, "N/A")
        final_data_list.append(combined_info)

    html_output = create_html_report(final_data_list)

    # Step 5: Create Excel file for download
    df = pd.DataFrame(final_data_list)
    excel_columns = [
        'gstin', 'trade_name', 'category', 'audit_group_number', 'audit_para_number',
        'audit_para_heading', 'harmonised_audit_para_heading', 'revenue_involved_lakhs_rs',
        'revenue_recovered_lakhs_rs', 'status_of_para', 'total_amount_detected_overall_rs',
        'total_amount_recovered_overall_rs'
    ]
    df = df.reindex(columns=excel_columns).fillna('N/A')

    output_excel = BytesIO()
    df.to_excel(output_excel, index=False, sheet_name='DAR_Extraction')
    output_excel.seek(0)

    excel_file_name = "dar_extraction_report.xlsx"
    with open(excel_file_name, "wb") as f:
        f.write(output_excel.getbuffer())

    return "Processing complete.", html_output, gr.File(value=excel_file_name), f"Requests today: {get_request_count()}/400"

# --- Gradio Interface Definition ---
with gr.Blocks(theme=gr.themes.Soft(), title="DAR Harmonisation Tool") as demo:

    # --- Login UI (visible initially) ---
    with gr.Column(visible=True) as login_ui:
        gr.Markdown("# Mumbai CGST Audit Officer  Login")
        gr.Markdown("Please enter the credentials to access the tool.")
        with gr.Row():
            username_input = gr.Textbox(label="Username", placeholder="Enter your username")
            password_input = gr.Textbox(label="Password", type="password", placeholder="Enter your password")
        login_button = gr.Button("Login", variant="primary")
        login_error_msg = gr.Markdown(visible=False)

    # --- Main App UI (hidden initially) ---
    with gr.Column(visible=False) as main_app_ui:
        gr.Markdown("# DAR Draft Audit Report Harmonisation Tool")
        gr.Markdown("## Initiative by Mumbai Audit 1 Commissionerate")
        gr.Markdown(
            "Upload a Departmental Audit Report (DAR) in PDF format. The tool will process it and generate harmonised titles for Audit paras in accordance with GST law."
        )
        request_count_output = gr.Textbox(label="Requests Made Today", interactive=False, value="Requests today: 0/400")
        with gr.Row():
            with gr.Column(scale=1):
                pdf_input = gr.File(label="Upload DAR PDF", file_types=[".pdf"])
                submit_btn = gr.Button("Process Report", variant="primary")
            with gr.Column(scale=2):
                status_output = gr.Textbox(label="Processing Status", interactive=False)
                excel_output = gr.File(label="Download Excel Report")
        gr.Markdown("## Harmonised Audit Para Titles")
        html_output = gr.HTML()

        submit_btn.click(
            fn=process_dar_pdf,
            inputs=[pdf_input],
            outputs=[status_output, html_output, excel_output, request_count_output]
        )

    # --- Login Functionality ---
    def login(username, password):
        """
        Checks user credentials against secrets.
        For production, these are loaded from Hugging Face secrets.
        """
        auth_username = os.environ.get("APP_USERNAME")
        auth_password = os.environ.get("APP_PASSWORD")

        is_valid_user = (username == auth_username and password == auth_password)

        if is_valid_user:
            # Login successful: hide login UI, show main app, display request count
            request_count = get_request_count()
            return {
                login_ui: gr.update(visible=False),
                main_app_ui: gr.update(visible=True),
                login_error_msg: gr.update(visible=False),
                request_count_output: gr.update(value=f"Requests today: {request_count}/400")
            }
        else:
            # Login failed: keep login UI visible, show error message
            return {
                login_ui: gr.update(visible=True),
                main_app_ui: gr.update(visible=False),
                login_error_msg: gr.update(value="<p style='color:red;'>Invalid username or password.</p>", visible=True),
                request_count_output: gr.update(value="Requests today: 0/400")
            }

    login_button.click(
        login,
        inputs=[username_input, password_input],
        outputs=[login_ui, main_app_ui, login_error_msg, request_count_output]
    )

if __name__ == "__main__":
    demo.launch(debug=True)# import gradio as gr
# import pandas as pd
# from io import BytesIO
# import os
# import json
# from datetime import datetime
# import firebase_admin
# from firebase_admin import credentials, firestore
# from dar_processor import preprocess_pdf_text
# from gemini_utils import get_structured_data_with_gemini, get_harmonised_titles
# from models import ParsedDARReport, HarmonisedPara

# # Firebase setup
# FIREBASE_CREDENTIALS = os.environ.get("FIREBASE_CREDENTIALS")
# if FIREBASE_CREDENTIALS:
#     # Load credentials from environment variable (preferred for security)
#     cred = credentials.Certificate(json.loads(FIREBASE_CREDENTIALS))
# else:
#     # Fallback to reading from firebase.json file
#     if not os.path.exists("firebase.json"):
#         raise ValueError("firebase.json not found and FIREBASE_CREDENTIALS not set.")
#     cred = credentials.Certificate("firebase.json")

# firebase_admin.initialize_app(cred)
# db = firestore.client()
# request_counts = db.collection('request_counts')

# def get_request_count():
#     """Retrieve the current request count for today."""
#     today = datetime.utcnow().strftime('%Y-%m-%d')
#     doc_ref = request_counts.document(today)
#     doc = doc_ref.get()
#     count = doc.to_dict().get('count', 0) if doc.exists else 0
#     return count

# def check_request_limit():
#     """Check if the request limit for the day has been reached."""
#     today = datetime.utcnow().strftime('%Y-%m-%d')
#     doc_ref = request_counts.document(today)
#     doc = doc_ref.get()

#     if not doc.exists:
#         # Initialize counter for the new day
#         doc_ref.set({'count': 0})
#         count = 0
#     else:
#         count = doc.to_dict().get('count', 0)

#     if count >= 400:
#         return False, "Daily request limit of 400 reached. Try again tomorrow."

#     # Increment the counter
#     doc_ref.update({'count': firestore.Increment(1)})
#     return True, None

# def create_html_report(results_with_harmonised: list[dict]) -> str:
#     """Generates an HTML string to display the results in a styled table."""
#     if not results_with_harmonised:
#         return "<p>No audit paras found or processed.</p>"
#     style = """
#     <style>
#         body { font-family: sans-serif; }
#         .styled-table {
#             border-collapse: collapse; margin: 25px 0; font-size: 0.9em;
#             min-width: 400px; box-shadow: 0 0 20px rgba(0, 0, 0, 0.15);
#             border-radius: 8px; overflow: hidden;
#         }
#         .styled-table thead tr { background-color: #009879; color: #ffffff; text-align: left; }
#         .styled-table th, .styled-table td { padding: 12px 15px; border-bottom: 1px solid #dddddd; }
#         .styled-table tbody tr:last-of-type { border-bottom: 2px solid #009879; }
#     </style>
#     """
#     html = f"{style}<table class='styled-table'><thead><tr><th>Para No.</th><th>Original Audit Para Heading</th><th>Harmonised Audit Para Heading</th><th>Amount Involved (in Lakhs)</th></tr></thead><tbody>"
#     for item in results_with_harmonised:
#         para_num = item.get('audit_para_number', 'N/A')
#         original_heading = item.get('audit_para_heading', 'N/A')
#         harmonised_heading = item.get('harmonised_audit_para_heading', 'N/A')
#         amount = f"₹{item.get('revenue_involved_lakhs_rs', 0.0):,.2f} L"
#         html += f"<tr><td>{para_num}</td><td>{original_heading}</td><td>{harmonised_heading}</td><td>{amount}</td></tr>"
#     html += "</tbody></table>"
#     return html

# def process_dar_pdf(pdf_file):
#     """The main processing function, called after successful login."""
#     # Check request limit before processing
#     can_process, error_msg = check_request_limit()
#     if not can_process:
#         return error_msg, None, None, f"Requests today: {get_request_count()}/400"

#     gemini_api_key = os.environ.get("GEMINI_API_KEY")
#     if not pdf_file:
#         return "Please upload a PDF file.", None, None, f"Requests today: {get_request_count()}/400"
#     if not gemini_api_key:
#         return "Error: GEMINI_API_KEY secret not found in Space settings.", None, None, f"Requests today: {get_request_count()}/400"

#     # Step 1: Process PDF to text
#     full_text = preprocess_pdf_text(pdf_file.name)
#     if full_text.startswith("Error"):
#         return f"Failed to process PDF: {full_text}", None, None, f"Requests today: {get_request_count()}/400"

#     # Step 2: Extract structured data
#     parsed_report = get_structured_data_with_gemini(gemini_api_key, full_text)
#     if parsed_report.parsing_errors or not parsed_report.audit_paras:
#         error_msg = parsed_report.parsing_errors or "Could not find any audit paras."
#         return error_msg, None, None, f"Requests today: {get_request_count()}/400"

#     # Step 3: Get harmonised titles
#     original_headings = [p.audit_para_heading for p in parsed_report.audit_paras if p.audit_para_heading]
#     if not original_headings:
#         return "Found paras but no headings to harmonise.", None, None, f"Requests today: {get_request_count()}/400"

#     harmonised_results = get_harmonised_titles(gemini_api_key, full_text, original_headings)
#     if not harmonised_results:
#         return "Failed to generate harmonised titles.", None, None, f"Requests today: {get_request_count()}/400"

#     # Step 4: Combine and prepare outputs
#     harmonised_map = {item.original_heading: item.harmonised_heading for item in harmonised_results}
#     final_data_list = []
#     for para in parsed_report.audit_paras:
#         combined_info = (parsed_report.header.dict() if parsed_report.header else {}) | para.dict()
#         combined_info['harmonised_audit_para_heading'] = harmonised_map.get(para.audit_para_heading, "N/A")
#         final_data_list.append(combined_info)

#     html_output = create_html_report(final_data_list)

#     # Step 5: Create Excel file for download
#     df = pd.DataFrame(final_data_list)
#     excel_columns = [
#         'gstin', 'trade_name', 'category', 'audit_group_number', 'audit_para_number',
#         'audit_para_heading', 'harmonised_audit_para_heading', 'revenue_involved_lakhs_rs',
#         'revenue_recovered_lakhs_rs', 'status_of_para', 'total_amount_detected_overall_rs',
#         'total_amount_recovered_overall_rs'
#     ]
#     df = df.reindex(columns=excel_columns).fillna('N/A')

#     output_excel = BytesIO()
#     df.to_excel(output_excel, index=False, sheet_name='DAR_Extraction')
#     output_excel.seek(0)

#     excel_file_name = "dar_extraction_report.xlsx"
#     with open(excel_file_name, "wb") as f:
#         f.write(output_excel.getbuffer())

#     return "Processing complete.", html_output, gr.File(value=excel_file_name), f"Requests today: {get_request_count()}/400"

# # --- Gradio Interface Definition ---
# with gr.Blocks(theme=gr.themes.Soft(), title="DAR Harmonisation Tool") as demo:

#     # --- Login UI (visible initially) ---
#     with gr.Column(visible=True) as login_ui:
#         gr.Markdown("# Audit Officer Login")
#         gr.Markdown("Please enter the credentials to access the tool.")
#         with gr.Row():
#             username_input = gr.Textbox(label="Username", placeholder="Enter your username")
#             password_input = gr.Textbox(label="Password", type="password", placeholder="Enter your password")
#         login_button = gr.Button("Login", variant="primary")
#         login_error_msg = gr.Markdown(visible=False)

#     # --- Main App UI (hidden initially) ---
#     with gr.Column(visible=False) as main_app_ui:
#         gr.Markdown("# DAR Draft Audit Report Harmonisation Tool")
#         gr.Markdown("## Initiative by Mumbai Audit 1 Commissionerate")
#         gr.Markdown(
#             "Upload a Observation letter to Taxpayer or  Departmental Audit Report (DAR) in PDF format. The tool will process it and generate harmonised titles for Audit paras in accordance with GST law."
#         )
#         request_count_output = gr.Textbox(label="Requests Made Today", interactive=False, value="Requests today: 0/400")
#         with gr.Row():
#             with gr.Column(scale=1):
#                 pdf_input = gr.File(label="Upload DAR PDF", file_types=[".pdf"])
#                 submit_btn = gr.Button("Process Report", variant="primary")
#             with gr.Column(scale=2):
#                 status_output = gr.Textbox(label="Processing Status", interactive=False)
#                 excel_output = gr.File(label="Download Excel Report")
#         gr.Markdown("## Harmonised Audit Para Titles")
#         html_output = gr.HTML()

#         submit_btn.click(
#             fn=process_dar_pdf,
#             inputs=[pdf_input],
#             outputs=[status_output, html_output, excel_output, request_count_output]
#         )

#     # --- Login Functionality ---
#     def login(username, password):
#         """
#         Checks user credentials against secrets.
#         For production, these are loaded from Hugging Face secrets.
#         """
#         auth_username = os.environ.get("APP_USERNAME")
#         auth_password = os.environ.get("APP_PASSWORD")

#         is_valid_user = (username == auth_username and password == auth_password)

#         if is_valid_user:
#             # Login successful: hide login UI, show main app, display request count
#             request_count = get_request_count()
#             return {
#                 login_ui: gr.update(visible=False),
#                 main_app_ui: gr.update(visible=True),
#                 login_error_msg: gr.update(visible=False),
#                 request_count_output: gr.update(value=f"Requests today: {request_count}/400")
#             }
#         else:
#             # Login failed: keep login UI visible, show error message
#             return {
#                 login_ui: gr.update(visible=True),
#                 main_app_ui: gr.update(visible=False),
#                 login_error_msg: gr.update(value="<p style='color:red;'>Invalid username or password.</p>", visible=True),
#                 request_count_output: gr.update(value="Requests today: 0/400")
#             }

#     login_button.click(
#         login,
#         inputs=[username_input, password_input],
#         outputs=[login_ui, main_app_ui, login_error_msg, request_count_output]
#     )

# if __name__ == "__main__":
#     demo.launch(debug=True)
# # import pandas as pd
# # from io import BytesIO
# # import os

# # # These imports assume the other python files (dar_processor.py, etc.) are in the same directory.
# # from dar_processor import preprocess_pdf_text
# # from gemini_utils import get_structured_data_with_gemini, get_harmonised_titles
# # from models import ParsedDARReport, HarmonisedPara

# # def create_html_report(results_with_harmonised: list[dict]) -> str:
# #     """Generates an HTML string to display the results in a styled table."""
# #     if not results_with_harmonised:
# #         return "<p>No audit paras found or processed.</p>"
# #     style = """
# #     <style>
# #         body { font-family: sans-serif; }
# #         .styled-table {
# #             border-collapse: collapse; margin: 25px 0; font-size: 0.9em;
# #             min-width: 400px; box-shadow: 0 0 20px rgba(0, 0, 0, 0.15);
# #             border-radius: 8px; overflow: hidden;
# #         }
# #         .styled-table thead tr { background-color: #009879; color: #ffffff; text-align: left; }
# #         .styled-table th, .styled-table td { padding: 12px 15px; border-bottom: 1px solid #dddddd; }
# #         .styled-table tbody tr:last-of-type { border-bottom: 2px solid #009879; }
# #     </style>
# #     """
# #     html = f"{style}<table class='styled-table'><thead><tr><th>Para No.</th><th>Original Audit Para Heading</th><th>Harmonised Audit Para Heading</th><th>Amount Involved (in Lakhs)</th></tr></thead><tbody>"
# #     for item in results_with_harmonised:
# #         para_num = item.get('audit_para_number', 'N/A')
# #         original_heading = item.get('audit_para_heading', 'N/A')
# #         harmonised_heading = item.get('harmonised_audit_para_heading', 'N/A')
# #         amount = f"₹{item.get('revenue_involved_lakhs_rs', 0.0):,.2f} L"
# #         html += f"<tr><td>{para_num}</td><td>{original_heading}</td><td>{harmonised_heading}</td><td>{amount}</td></tr>"
# #     html += "</tbody></table>"
# #     return html

# # def process_dar_pdf(pdf_file):
# #     """The main processing function, called after successful login."""
# #     gemini_api_key = os.environ.get("GEMINI_API_KEY")
# #     if not pdf_file:
# #         return "Please upload a PDF file.", None, None
# #     if not gemini_api_key:
# #         return "Error: GEMINI_API_KEY secret not found in Space settings.", None, None

# #     # Step 1: Process PDF to text
# #     full_text = preprocess_pdf_text(pdf_file.name)
# #     if full_text.startswith("Error"):
# #         return f"Failed to process PDF: {full_text}", None, None

# #     # Step 2: Extract structured data
# #     parsed_report = get_structured_data_with_gemini(gemini_api_key, full_text)
# #     if parsed_report.parsing_errors or not parsed_report.audit_paras:
# #         error_msg = parsed_report.parsing_errors or "Could not find any audit paras."
# #         return error_msg, None, None

# #     # Step 3: Get harmonised titles
# #     original_headings = [p.audit_para_heading for p in parsed_report.audit_paras if p.audit_para_heading]
# #     if not original_headings:
# #         return "Found paras but no headings to harmonise.", None, None
        
# #     harmonised_results = get_harmonised_titles(gemini_api_key, full_text, original_headings)
# #     if not harmonised_results:
# #         return "Failed to generate harmonised titles.", None, None

# #     # Step 4: Combine and prepare outputs
# #     harmonised_map = {item.original_heading: item.harmonised_heading for item in harmonised_results}
# #     final_data_list = []
# #     for para in parsed_report.audit_paras:
# #         combined_info = (parsed_report.header.dict() if parsed_report.header else {}) | para.dict()
# #         combined_info['harmonised_audit_para_heading'] = harmonised_map.get(para.audit_para_heading, "N/A")
# #         final_data_list.append(combined_info)
    
# #     html_output = create_html_report(final_data_list)
    
# #     # Step 5: Create Excel file for download
# #     df = pd.DataFrame(final_data_list)
# #     excel_columns = [
# #         'gstin', 'trade_name', 'category', 'audit_group_number', 'audit_para_number', 
# #         'audit_para_heading', 'harmonised_audit_para_heading', 'revenue_involved_lakhs_rs', 
# #         'revenue_recovered_lakhs_rs', 'status_of_para', 'total_amount_detected_overall_rs', 
# #         'total_amount_recovered_overall_rs'
# #     ]
# #     df = df.reindex(columns=excel_columns).fillna('N/A')
    
# #     output_excel = BytesIO()
# #     df.to_excel(output_excel, index=False, sheet_name='DAR_Extraction')
# #     output_excel.seek(0)
    
# #     excel_file_name = "dar_extraction_report.xlsx"
# #     with open(excel_file_name, "wb") as f:
# #         f.write(output_excel.getbuffer())

# #     return "Processing complete.", html_output, gr.File(value=excel_file_name)

# # # --- Gradio Interface Definition ---
# # with gr.Blocks(theme=gr.themes.Soft(), title="DAR Harmonisation Tool") as demo:

# #     # --- Login UI (visible initially) ---
# #     with gr.Column(visible=True) as login_ui:
# #         gr.Markdown("# Audit Officer Login")
# #         gr.Markdown("Please enter the credentials to access the tool.")
# #         with gr.Row():
# #             username_input = gr.Textbox(label="Username", placeholder="Enter your username")
# #             password_input = gr.Textbox(label="Password", type="password", placeholder="Enter your password")
# #         login_button = gr.Button("Login", variant="primary")
# #         login_error_msg = gr.Markdown(visible=False)

# #     # --- Main App UI (hidden initially) ---
# #     with gr.Column(visible=False) as main_app_ui:
# #         gr.Markdown("# DAR Draft Audit Report Harmonisation Tool")
# #         gr.Markdown("## Initiative by Mumbai Audit 1 Commissionerate")
# #         gr.Markdown(
# #             "Upload a Observation letter to taxpayer or Departmental Audit Report (DAR) in PDF format. The tool will process it and generate harmonised titles for Audit paras in accordance with GST law."
           
# #         )
# #         with gr.Row():
# #             with gr.Column(scale=1):
# #                 pdf_input = gr.File(label="Upload DAR PDF", file_types=[".pdf"])
# #                 submit_btn = gr.Button("Process Report", variant="primary")
# #             with gr.Column(scale=2):
# #                 status_output = gr.Textbox(label="Processing Status", interactive=False)
# #                 excel_output = gr.File(label="Download Excel Report")
# #         gr.Markdown("## Harmonised Audit Para Titles")
# #         html_output = gr.HTML()

# #         submit_btn.click(
# #             fn=process_dar_pdf,
# #             inputs=[pdf_input],
# #             outputs=[status_output, html_output, excel_output]
# #         )

# #     # --- Login Functionality ---
# #     def login(username, password):
# #         """
# #         Checks user credentials against secrets.
# #         For production, these are loaded from Hugging Face secrets.
# #         """
# #         # Get credentials from Hugging Face secrets.
# #         # Fallback to default values for local testing if secrets are not set.
# #         auth_username = os.environ.get("APP_USERNAME")
# #         auth_password = os.environ.get("APP_PASSWORD")
        
# #         is_valid_user = (username == auth_username and password == auth_password)
        
# #         if is_valid_user:
# #             # Login successful: hide login UI, show main app
# #             return {
# #                 login_ui: gr.update(visible=False),
# #                 main_app_ui: gr.update(visible=True),
# #                 login_error_msg: gr.update(visible=False)
# #             }
# #         else:
# #             # Login failed: keep login UI visible, show error message
# #             return {
# #                 login_ui: gr.update(visible=True),
# #                 main_app_ui: gr.update(visible=False),
# #                 login_error_msg: gr.update(value="<p style='color:red;'>Invalid username or password.</p>", visible=True)
# #             }

# #     login_button.click(
# #         login,
# #         inputs=[username_input, password_input],
# #         outputs=[login_ui, main_app_ui, login_error_msg]
# #     )

# # if __name__ == "__main__":
# #     demo.launch(debug=True)