import pandas as pd
import re

# Sample unstructured text containing dollar amounts
claims_data = pd.DataFrame({
    'Claim_ID': [1001, 1002, 1003, 1004],
    'Description': [
        "Customer reported theft of laptop valued at $2,500 and iPhone worth $800 from vehicle on Main Street.",
        "Fire damage to office equipment including printer ($1,200), monitor ($350), and software licenses totaling $4,500.",
        "Water damage claim for $12,000.50 covering furniture replacement and $850 in cleaning services.",
        "Vehicle accident resulting in repairs estimated at $8,750 with additional rental car costs of $450.00."
    ]
})

print("Original Claims Data:")
print(claims_data)
print()

# Step 1: Define regex pattern to find dollar amounts
# Pattern explanation:
# \$ - literal dollar sign
# (?:\d{1,3}(?:,\d{3})*|\d+) - numbers with optional commas (1,000 or 1000)
# (?:\.\d{2})? - optional decimal part (.50)
dollar_pattern = r'\$(?:\d{1,3}(?:,\d{3})*|\d+)(?:\.\d{2})?'

# Step 2: Extract all dollar amounts from each description
def extract_dollar_amounts(text):
    """Extract all dollar amounts from text and return as list"""
    matches = re.findall(dollar_pattern, text)
    return matches

# Apply extraction to each row
claims_data['Dollar_Amounts_Found'] = claims_data['Description'].apply(extract_dollar_amounts)

print("Dollar amounts found in each claim:")
print(claims_data[['Claim_ID', 'Dollar_Amounts_Found']])
print()

# Step 3: Convert extracted amounts to numeric values
def clean_dollar_amount(amount_str):
    """Convert '$1,200.50' to 1200.50"""
    # Remove $ and commas, convert to float
    return float(amount_str.replace('$', '').replace(',', ''))

# Step 4: Calculate total for each claim
def calculate_claim_total(amounts_list):
    """Sum all dollar amounts in the list"""
    if not amounts_list:  # Handle empty lists
        return 0.0
    return sum(clean_dollar_amount(amount) for amount in amounts_list)

claims_data['Total_Amount'] = claims_data['Dollar_Amounts_Found'].apply(calculate_claim_total)

print("Claims with extracted totals:")
print(claims_data[['Claim_ID', 'Total_Amount']])
print()

# Step 5: Create detailed breakdown
detailed_breakdown = []
for _, row in claims_data.iterrows():
    claim_id = row['Claim_ID']
    for amount_str in row['Dollar_Amounts_Found']:
        detailed_breakdown.append({
            'Claim_ID': claim_id,
            'Amount_Text': amount_str,
            'Amount_Numeric': clean_dollar_amount(amount_str)
        })

breakdown_df = pd.DataFrame(detailed_breakdown)

print("Detailed breakdown of all extracted amounts:")
print(breakdown_df)
print()

# Step 6: Generate summary report
summary_stats = pd.DataFrame({
    'Metric': [
        'Total Claims Processed',
        'Total Dollar Amounts Found', 
        'Average Amounts per Claim',
        'Total Value of All Claims',
        'Largest Single Amount',
        'Smallest Single Amount'
    ],
    'Value': [
        len(claims_data),
        len(breakdown_df),
        round(len(breakdown_df) / len(claims_data), 1),
        f"${breakdown_df['Amount_Numeric'].sum():,.2f}",
        f"${breakdown_df['Amount_Numeric'].max():,.2f}",
        f"${breakdown_df['Amount_Numeric'].min():,.2f}"
    ]
})

print("Insurance Claims Summary Report:")
print(summary_stats.to_string(index=False))
print()

# Step 7: Show regex pattern testing
print("Testing regex pattern with various formats:")
test_amounts = [
    "$1,500", "$250.75", "$12,000.50", "$5", "$999,999.99", 
    "not money", "$0.99", "$1,234,567"
]

for test in test_amounts:
    matches = re.findall(dollar_pattern, test)
    print(f"'{test}' → Found: {matches}")