Update app.py

app.py

@@ -148,7 +148,7 @@ 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("# DAR Harmonisation Tool Login")
         gr.Markdown("Please enter the credentials to access the tool.")
         with gr.Row():
             username_input = gr.Textbox(label="Username", placeholder="Enter your username")
@@ -161,7 +161,7 @@ with gr.Blocks(theme=gr.themes.Soft(), title="DAR Harmonisation Tool") as demo:
         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."
+            "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():
@@ -216,16 +216,61 @@ with gr.Blocks(theme=gr.themes.Soft(), title="DAR Harmonisation Tool") as demo:
     )
 
 if __name__ == "__main__":
-    demo.launch(debug=True)
+    demo.launch(debug=True)# import gradio as gr
 # 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.
+# 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:
@@ -255,31 +300,36 @@ if __name__ == "__main__":
 
 # 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
+#         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
+#         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
+#         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
+#         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
-        
+#         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
+#         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}
@@ -288,28 +338,28 @@ if __name__ == "__main__":
 #         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', 
+#         '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)
+#     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:
@@ -329,9 +379,9 @@ if __name__ == "__main__":
 #         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."
-           
+#             "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"])
@@ -345,7 +395,7 @@ if __name__ == "__main__":
 #         submit_btn.click(
 #             fn=process_dar_pdf,
 #             inputs=[pdf_input],
-#             outputs=[status_output, html_output, excel_output]
+#             outputs=[status_output, html_output, excel_output, request_count_output]
 #         )
 
 #     # --- Login Functionality ---
@@ -354,33 +404,201 @@ if __name__ == "__main__":
 #         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
+#             # 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)
+#                 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)
+#                 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]
+#         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)