Update app.py

app.py

@@ -1,355 +1,3 @@
-# import os
-# import re
-# import json
-# import gradio as gr
-# import pandas as pd
-# import pdfplumber
-# import pytesseract
-# from pdf2image import convert_from_path
-# from huggingface_hub import InferenceClient
-
-# # Initialize with reliable free model
-# hf_token = os.getenv("HF_TOKEN")
-# client = InferenceClient(model="mistralai/Mistral-7B-Instruct-v0.2", token=hf_token)
-
-# def extract_excel_data(file_path):
-#     """Extract text from Excel file"""
-#     df = pd.read_excel(file_path, engine='openpyxl')
-#     return df.to_string(index=False)
-
-# def extract_text_from_pdf(pdf_path, is_scanned=False):
-#     """Extract text from PDF with fallback OCR"""
-#     try:
-#         # Try native PDF extraction first
-#         with pdfplumber.open(pdf_path) as pdf:
-#             text = ""
-#             for page in pdf.pages:
-#                 # Extract tables first for structured data
-#                 tables = page.extract_tables()
-#                 for table in tables:
-#                     for row in table:
-#                         text += " | ".join(str(cell) for cell in row) + "\n"
-#                     text += "\n"
-                
-#                 # Extract text for unstructured data
-#                 page_text = page.extract_text()
-#                 if page_text:
-#                     text += page_text + "\n\n"
-#             return text
-#     except Exception as e:
-#         print(f"Native PDF extraction failed: {str(e)}")
-#         # Fallback to OCR for scanned PDFs
-#         images = convert_from_path(pdf_path, dpi=200)
-#         text = ""
-#         for image in images:
-#             text += pytesseract.image_to_string(image) + "\n"
-#         return text
-
-# def parse_bank_statement(text, file_type):
-#     """Parse bank statement using LLM with fallback to rule-based parser"""
-#     # Clean text differently based on file type
-#     cleaned_text = re.sub(r'[\x00-\x08\x0b\x0c\x0e-\x1f\x7f]', '', text)
-    
-#     if file_type == 'pdf':
-#         # PDF-specific cleaning
-#         cleaned_text = re.sub(r'Page \d+ of \d+', '', cleaned_text, flags=re.IGNORECASE)
-#         cleaned_text = re.sub(r'CropBox.*?MediaBox', '', cleaned_text, flags=re.IGNORECASE)
-        
-#         # Keep only lines that look like transactions
-#         transaction_lines = []
-#         for line in cleaned_text.split('\n'):
-#             if re.match(r'^\d{4}-\d{2}-\d{2}', line):  # Date pattern
-#                 transaction_lines.append(line)
-#             elif '|' in line and any(x in line for x in ['Date', 'Amount', 'Balance']):
-#                 transaction_lines.append(line)
-        
-#         cleaned_text = "\n".join(transaction_lines)
-    
-#     print(f"Cleaned text sample: {cleaned_text[:200]}...")
-    
-#     # Try rule-based parsing first for structured data
-#     rule_based_data = rule_based_parser(cleaned_text)
-#     if rule_based_data["transactions"]:
-#         print("Using rule-based parser results")
-#         return rule_based_data
-    
-#     # Fallback to LLM for unstructured data
-#     print("Falling back to LLM parsing")
-#     return llm_parser(cleaned_text)
-
-# def llm_parser(text):
-#     """LLM parser for unstructured text"""
-#     # Craft precise prompt with strict JSON formatting instructions
-#     prompt = f"""
-# <|system|>
-# You are a financial data parser. Extract transactions from bank statements and return ONLY valid JSON.
-# </s>
-# <|user|>
-# Extract all transactions from this bank statement with these exact fields:
-# - date (format: YYYY-MM-DD)
-# - description
-# - amount (format: 0.00)
-# - debit (format: 0.00)
-# - credit (format: 0.00)
-# - closing_balance (format: 0.00 or -0.00 for negative)
-# - category
-
-# Statement text:
-# {text[:3000]}  [truncated if too long]
-
-# Return JSON with this exact structure:
-# {{
-#   "transactions": [
-#     {{
-#       "date": "2025-05-08",
-#       "description": "Company XYZ Payroll",
-#       "amount": "8315.40",
-#       "debit": "0.00",
-#       "credit": "8315.40",
-#       "closing_balance": "38315.40",
-#       "category": "Salary"
-#     }}
-#   ]
-# }}
-
-# RULES:
-# 1. Output ONLY the JSON object with no additional text
-# 2. Keep amounts as strings with 2 decimal places
-# 3. For missing values, use empty strings
-# 4. Convert negative amounts to format "-123.45"
-# 5. Map categories to: Salary, Groceries, Medical, Utilities, Entertainment, Dining, Misc
-# </s>
-# <|assistant|>
-# """
-    
-#     try:
-#         # Call LLM via Hugging Face Inference API
-#         response = client.text_generation(
-#             prompt,
-#             max_new_tokens=2000,
-#             temperature=0.01,
-#             stop=["</s>"]  # Updated to 'stop' parameter
-#         )
-#         print(f"LLM Response: {response}")
-        
-#         # Validate and clean JSON response
-#         response = response.strip()
-#         if not response.startswith('{'):
-#             # Find the first { and last } to extract JSON
-#             start_idx = response.find('{')
-#             end_idx = response.rfind('}')
-#             if start_idx != -1 and end_idx != -1:
-#                 response = response[start_idx:end_idx+1]
-        
-#         # Parse JSON and validate structure
-#         data = json.loads(response)
-#         if "transactions" not in data:
-#             raise ValueError("Missing 'transactions' key in JSON")
-            
-#         return data
-#     except Exception as e:
-#         print(f"LLM Error: {str(e)}")
-#         return {"transactions": []}
-
-# def rule_based_parser(text):
-#     """Enhanced fallback parser for structured tables"""
-#     lines = [line.strip() for line in text.split('\n') if line.strip()]
-    
-#     # Find header line - more flexible detection
-#     header_index = None
-#     header_patterns = [
-#         r'Date\b', r'Description\b', r'Amount\b', 
-#         r'Debit\b', r'Credit\b', r'Closing\s*Balance\b', r'Category\b'
-#     ]
-    
-#     # First try: Look for a full header line
-#     for i, line in enumerate(lines):
-#         if all(re.search(pattern, line, re.IGNORECASE) for pattern in header_patterns[:3]):
-#             header_index = i
-#             break
-    
-#     # Second try: Look for any header indicators
-#     if header_index is None:
-#         for i, line in enumerate(lines):
-#             if any(re.search(pattern, line, re.IGNORECASE) for pattern in header_patterns):
-#                 header_index = i
-#                 break
-    
-#     # Third try: Look for pipe-delimited headers
-#     if header_index is None:
-#         for i, line in enumerate(lines):
-#             if '|' in line and any(p in line for p in ['Date', 'Amount', 'Balance']):
-#                 header_index = i
-#                 break
-    
-#     if header_index is None:
-#         return {"transactions": []}
-    
-#     data_lines = lines[header_index + 1:]
-#     transactions = []
-    
-#     for line in data_lines:
-#         # Handle both pipe-delimited and space-delimited formats
-#         if '|' in line:
-#             parts = [p.strip() for p in line.split('|') if p.strip()]
-#         else:
-#             # Space-delimited format - split by 2+ spaces
-#             parts = re.split(r'\s{2,}', line)
-        
-#         # Skip lines that don't have enough parts
-#         if len(parts) < 7:
-#             continue
-            
-#         try:
-#             # Handle transaction date validation
-#             if not re.match(r'\d{4}-\d{2}-\d{2}', parts[0]):
-#                 continue
-                
-#             transactions.append({
-#                 "date": parts[0],
-#                 "description": parts[1],
-#                 "amount": format_number(parts[2]),
-#                 "debit": format_number(parts[3]),
-#                 "credit": format_number(parts[4]),
-#                 "closing_balance": format_number(parts[5]),
-#                 "category": parts[6]
-#             })
-#         except Exception as e:
-#             print(f"Error parsing line: {str(e)}")
-    
-#     return {"transactions": transactions}
-
-# def format_number(value):
-#     """Format numeric values consistently"""
-#     if not value or str(value).lower() in ['nan', 'nat']:
-#         return "0.00"
-        
-#     # If it's already a number, format directly
-#     if isinstance(value, (int, float)):
-#         return f"{value:.2f}"
-        
-#     # Clean string values
-#     value = str(value).replace(',', '').replace('$', '').strip()
-    
-#     # Handle negative numbers in parentheses
-#     if '(' in value and ')' in value:
-#         value = '-' + value.replace('(', '').replace(')', '')
-    
-#     # Handle empty values
-#     if not value:
-#         return "0.00"
-    
-#     # Standardize decimal format
-#     if '.' not in value:
-#         value += '.00'
-    
-#     # Ensure two decimal places
-#     try:
-#         num_value = float(value)
-#         return f"{num_value:.2f}"
-#     except ValueError:
-#         # If we can't convert to float, return original but clean it
-#         return value.split('.')[0] + '.' + value.split('.')[1][:2].ljust(2, '0')
-
-# def process_file(file, is_scanned):
-#     """Main processing function"""
-#     if not file:
-#         return empty_df()
-    
-#     file_path = file.name
-#     file_ext = os.path.splitext(file_path)[1].lower()
-    
-#     try:
-#         if file_ext == '.xlsx':
-#             # Directly process Excel files without text conversion
-#             df = pd.read_excel(file_path, engine='openpyxl')
-            
-#             # Normalize column names
-#             df.columns = df.columns.str.strip().str.lower()
-            
-#             # Create mapping to expected columns
-#             col_mapping = {
-#                 'date': 'date',
-#                 'description': 'description',
-#                 'amount': 'amount',
-#                 'debit': 'debit',
-#                 'credit': 'credit',
-#                 'closing balance': 'closing_balance',
-#                 'closing': 'closing_balance',
-#                 'balance': 'closing_balance',
-#                 'category': 'category'
-#             }
-            
-#             # Create output DataFrame with required columns
-#             output_df = pd.DataFrame()
-#             for col in ['date', 'description', 'amount', 'debit', 'credit', 'closing_balance', 'category']:
-#                 if col in df.columns:
-#                     output_df[col] = df[col]
-#                 elif any(alias in col_mapping and col_mapping[alias] == col for alias in df.columns):
-#                     # Find alias
-#                     for alias in df.columns:
-#                         if alias in col_mapping and col_mapping[alias] == col:
-#                             output_df[col] = df[alias]
-#                             break
-#                 else:
-#                     output_df[col] = ""
-            
-#             # Format numeric columns
-#             for col in ['amount', 'debit', 'credit', 'closing_balance']:
-#                 output_df[col] = output_df[col].apply(format_number)
-            
-#             # Rename columns for display
-#             output_df.columns = ["Date", "Description", "Amount", "Debit", 
-#                                "Credit", "Closing Balance", "Category"]
-#             return output_df
-
-#         elif file_ext == '.pdf':
-#             text = extract_text_from_pdf(file_path, is_scanned=is_scanned)
-#             parsed_data = parse_bank_statement(text, 'pdf')
-#             df = pd.DataFrame(parsed_data["transactions"])
-            
-#             # Ensure all required columns exist
-#             required_cols = ["date", "description", "amount", "debit", 
-#                             "credit", "closing_balance", "category"]
-#             for col in required_cols:
-#                 if col not in df.columns:
-#                     df[col] = ""
-            
-#             # Format columns properly
-#             df.columns = ["Date", "Description", "Amount", "Debit", 
-#                          "Credit", "Closing Balance", "Category"]
-#             return df
-        
-#         else:
-#             return empty_df()
-    
-#     except Exception as e:
-#         print(f"Processing error: {str(e)}")
-#         return empty_df()
-
-# def empty_df():
-#     """Return empty DataFrame with correct columns"""
-#     return pd.DataFrame(columns=["Date", "Description", "Amount", "Debit", 
-#                                "Credit", "Closing Balance", "Category"])
-
-# # Gradio Interface
-# interface = gr.Interface(
-#     fn=process_file,
-#     inputs=[
-#         gr.File(label="Upload Bank Statement (PDF/Excel)")
-#     ],
-#     outputs=gr.Dataframe(
-#         label="Parsed Transactions",
-#         headers=["Date", "Description", "Amount", "Debit", "Credit", "Closing Balance", "Category"],
-#         datatype=["date", "str", "number", "number", "number", "number", "str"]
-#     ),
-#     title="AI Bank Statement Parser",
-#     description="Extract structured transaction data from PDF/Excel bank statements",
-#     allow_flagging="never"
-# )
-
-# if __name__ == "__main__":
-#     interface.launch()
 import os
 import re
 import json