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Quick Start Demo Mode for IDP System
Works without trained models using rule-based extraction
Allows immediate testing while models train
"""
import re
from typing import Dict, List
import numpy as np
class DemoClassifier:
"""Simple rule-based classifier for demo"""
def predict(self, text: str) -> Dict:
text_lower = text.lower()
# Simple heuristics
if 'invoice' in text_lower:
return {
'predicted_class': 'INVOICE',
'confidence': 0.85,
'probabilities': {'INVOICE': 0.85, 'RECEIPT': 0.10, 'FORM': 0.03, 'OTHER': 0.02}
}
elif any(k in text_lower for k in ['bank statement', 'account statement', 'statement of account']):
return {
'predicted_class': 'BANK_STATEMENT',
'confidence': 0.90,
'probabilities': {'INVOICE': 0.05, 'RECEIPT': 0.05, 'FORM': 0.05, 'BANK_STATEMENT': 0.85}
}
elif 'receipt' in text_lower:
return {
'predicted_class': 'RECEIPT',
'confidence': 0.80,
'probabilities': {'INVOICE': 0.15, 'RECEIPT': 0.80, 'FORM': 0.03, 'OTHER': 0.02}
}
elif 'form' in text_lower or 'application' in text_lower:
return {
'predicted_class': 'FORM',
'confidence': 0.75,
'probabilities': {'INVOICE': 0.05, 'RECEIPT': 0.10, 'FORM': 0.75, 'OTHER': 0.10}
}
else:
# Fallback for things with prices/totals that aren't explicitly receipts
if 'total' in text_lower and '$' in text_lower:
return {
'predicted_class': 'RECEIPT',
'confidence': 0.60,
'probabilities': {'INVOICE': 0.20, 'RECEIPT': 0.60, 'FORM': 0.10, 'OTHER': 0.10}
}
return {
'predicted_class': 'OTHER',
'confidence': 0.70,
'probabilities': {'INVOICE': 0.10, 'RECEIPT': 0.10, 'FORM': 0.10, 'OTHER': 0.70}
}
class DemoNER:
"""Simple rule-based NER for demo"""
def __init__(self):
self.patterns = {
'INVOICE_NUMBER': [
r'INV[-\s]?\d+',
r'Invoice\s*#?\s*:?\s*([A-Z0-9\-]+)',
r'\b\d{6,}\b',
],
'DATE': [
r'\d{1,2}[/-]\d{1,2}[/-]\d{2,4}',
r'\d{4}[/-]\d{1,2}[/-]\d{1,2}',
],
'TOTAL_AMOUNT': [
r'Total\s*:?\s*[₹$€£]\s*[\d,]+\.?\d*',
r'[₹$€£]\s*[\d,]+\.?\d*',
],
'TAX_AMOUNT': [
r'Tax\s*:?\s*[₹$€£]\s*[\d,]+\.?\d*',
r'GST\s*:?\s*[₹$€£]\s*[\d,]+\.?\d*',
],
}
def predict(self, text: str) -> Dict:
entities = []
for entity_type, patterns in self.patterns.items():
for pattern in patterns:
matches = re.finditer(pattern, text, re.IGNORECASE)
for match in matches:
entities.append({
'entity': entity_type,
'text': match.group(0).strip(),
'confidence': 0.75,
'start': match.start(),
'end': match.end()
})
return {'entities': entities, 'text': text}
def create_demo_models_directory():
"""Create models directory with demo flag"""
import os
os.makedirs('models/classifier', exist_ok=True)
os.makedirs('models/ner', exist_ok=True)
# Create demo marker
with open('models/DEMO_MODE.txt', 'w') as f:
f.write('Using demo mode - rule-based extraction\n')
f.write('Train models with: python train_classifier.py && python train_ner.py\n')
print("✓ Demo mode initialized")
print(" Using rule-based extraction until models are trained")
if __name__ == "__main__":
create_demo_models_directory()
# Test demo
demo_text = """
INVOICE #INV-12345
Date: 28/11/2025
Total: $1,250.00
Tax: $125.00
"""
classifier = DemoClassifier()
ner = DemoNER()
print("\nDemo Classifier:")
print(classifier.predict(demo_text))
print("\nDemo NER:")
print(ner.predict(demo_text))
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