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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 📄 Phase 2: Bank Statement Row Extraction\n",
"\n",
"This notebook guides you through Phase 2 of the model upgrade:\n",
"- Extract text rows from bank statement PDFs\n",
"- Label rows with entities\n",
"- Generate synthetic variations\n",
"- Prepare training data with [BANK_STATEMENT] prefix\n",
"\n",
"## Goal\n",
"**Model parses bank statement rows with high accuracy**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 1: Setup & Check PDF Files\n",
"\n",
"Place your bank statements in: `data/raw/pdfs/statements/`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from pathlib import Path\n",
"import json\n",
"\n",
"# Setup directories\n",
"PROJECT_ROOT = Path.cwd()\n",
"PDF_DIR = PROJECT_ROOT / \"data/raw/pdfs/statements\"\n",
"LABELING_DIR = PROJECT_ROOT / \"data/labeling\"\n",
"TRAINING_DIR = PROJECT_ROOT / \"data/training\"\n",
"\n",
"# Create directories\n",
"PDF_DIR.mkdir(parents=True, exist_ok=True)\n",
"LABELING_DIR.mkdir(parents=True, exist_ok=True)\n",
"\n",
"# Check for PDFs\n",
"pdfs = list(PDF_DIR.glob(\"*.pdf\")) + list(PDF_DIR.glob(\"*.PDF\"))\n",
"print(f\"📂 Found {len(pdfs)} PDF files in {PDF_DIR}\")\n",
"for pdf in pdfs:\n",
" print(f\" • {pdf.name}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 2: Extract Rows from PDFs"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from src.data.statement_extractor import StatementRowExtractor\n",
"\n",
"extractor = StatementRowExtractor(debug=False)\n",
"\n",
"all_rows = []\n",
"\n",
"for pdf in pdfs:\n",
" print(f\"\\n📄 Processing: {pdf.name}\")\n",
" try:\n",
" rows, stats = extractor.extract_rows(pdf)\n",
" all_rows.extend(rows)\n",
" print(f\" ✅ Extracted {stats.valid_rows} rows ({stats.bank.upper()})\")\n",
" except Exception as e:\n",
" print(f\" ❌ Error: {e}\")\n",
"\n",
"print(f\"\\n📊 Total rows extracted: {len(all_rows)}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 3: Preview Extracted Rows"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"# Convert to DataFrame for easy viewing\n",
"df = pd.DataFrame([r.to_dict() for r in all_rows])\n",
"\n",
"# Display columns\n",
"display_cols = ['date', 'description', 'debit', 'credit', 'balance', 'bank']\n",
"cols = [c for c in display_cols if c in df.columns]\n",
"\n",
"print(f\"📋 Sample rows (first 10):\")\n",
"df[cols].head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 4: Export for Manual Labeling\n",
"\n",
"Export rows to JSON for manual entity labeling."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"output_file = LABELING_DIR / \"statement_rows_unlabeled.json\"\n",
"extractor.export_for_labeling(all_rows, output_file)\n",
"\n",
"print(f\"✅ Exported {len(all_rows)} rows to:\")\n",
"print(f\" {output_file}\")\n",
"print(f\"\\n📝 Next: Open the JSON file and add 'entities' to each row\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 5: Manual Labeling Guide\n",
"\n",
"For each row, add the `entities` field with:\n",
"\n",
"```json\n",
"{\n",
" \"raw_text\": \"01-12-2025 | UPI-SWIGGY@ybl | 250.00 | | 45,230.50\",\n",
" \"labeled\": true,\n",
" \"entities\": {\n",
" \"date\": \"01-12-2025\",\n",
" \"description\": \"UPI-SWIGGY@ybl\",\n",
" \"amount\": \"250.00\",\n",
" \"type\": \"debit\",\n",
" \"balance\": \"45,230.50\",\n",
" \"merchant\": \"swiggy\",\n",
" \"category\": \"food\"\n",
" }\n",
"}\n",
"```\n",
"\n",
"**Target: Label 500+ rows**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 6: Load Labeled Data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# After manual labeling, load the data\n",
"labeled_file = LABELING_DIR / \"statement_rows_labeled.json\"\n",
"\n",
"if labeled_file.exists():\n",
" labeled_rows = extractor.load_labeled_data(labeled_file)\n",
" labeled_count = sum(1 for r in labeled_rows if r.labeled)\n",
" print(f\"📊 Loaded {len(labeled_rows)} rows ({labeled_count} labeled)\")\n",
"else:\n",
" print(f\"⚠️ Labeled file not found: {labeled_file}\")\n",
" print(f\" Rename your labeled file to: statement_rows_labeled.json\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 7: Generate Synthetic Variations"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from src.data.statement_extractor import StatementSyntheticGenerator\n",
"\n",
"generator = StatementSyntheticGenerator(seed=42)\n",
"\n",
"# Generate variations from labeled data\n",
"if 'labeled_rows' in dir() and labeled_rows:\n",
" # Filter to labeled only\n",
" base_rows = [r for r in labeled_rows if r.labeled]\n",
" \n",
" # Generate 5x variations\n",
" synthetic_rows = generator.generate_variations(\n",
" base_rows, \n",
" variations_per_row=5,\n",
" total_limit=2000\n",
" )\n",
" \n",
" print(f\"✅ Generated {len(synthetic_rows)} synthetic variations\")\n",
"else:\n",
" print(\"⚠️ Load labeled data first (Step 6)\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 8: Export Training Data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from src.data.statement_extractor import export_training_data\n",
"\n",
"if 'synthetic_rows' in dir() and synthetic_rows:\n",
" # Combine labeled + synthetic\n",
" all_training = base_rows + synthetic_rows\n",
" \n",
" # Export\n",
" train_file, valid_file = export_training_data(\n",
" all_training,\n",
" TRAINING_DIR / \"statement\"\n",
" )\n",
" \n",
" print(f\"✅ Training files created:\")\n",
" print(f\" Train: {train_file}\")\n",
" print(f\" Valid: {valid_file}\")\n",
"else:\n",
" print(\"⚠️ Generate synthetic data first (Step 7)\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 9: Combine with Phase 1 Data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Combine Phase 1 and Phase 2 training data\n",
"phase1_train = TRAINING_DIR / \"train.jsonl\"\n",
"phase2_train = TRAINING_DIR / \"statement_train.jsonl\"\n",
"combined_train = TRAINING_DIR / \"combined_train.jsonl\"\n",
"\n",
"if phase1_train.exists() and phase2_train.exists():\n",
" # Read both files\n",
" with open(phase1_train) as f:\n",
" p1_data = f.readlines()\n",
" with open(phase2_train) as f:\n",
" p2_data = f.readlines()\n",
" \n",
" # Combine and shuffle\n",
" import random\n",
" combined = p1_data + p2_data\n",
" random.shuffle(combined)\n",
" \n",
" with open(combined_train, 'w') as f:\n",
" f.writelines(combined)\n",
" \n",
" print(f\"✅ Combined training data:\")\n",
" print(f\" Phase 1: {len(p1_data)} samples\")\n",
" print(f\" Phase 2: {len(p2_data)} samples\")\n",
" print(f\" Total: {len(combined)} samples\")\n",
"else:\n",
" print(f\"⚠️ Missing files:\")\n",
" print(f\" Phase 1: {phase1_train.exists()}\")\n",
" print(f\" Phase 2: {phase2_train.exists()}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 10: Retrain Model\n",
"\n",
"Run in terminal (not notebook):\n",
"\n",
"```bash\n",
"cd ~/llm-mail-trainer\n",
"source venv/bin/activate\n",
"\n",
"mlx_lm.lora \\\n",
" --model models/base/phi3-mini \\\n",
" --data data/training \\\n",
" --train \\\n",
" --batch-size 1 \\\n",
" --lora-layers 8 \\\n",
" --iters 800 \\\n",
" --adapter-path models/adapters/finance-lora-v4\n",
"```\n",
"\n",
"Note: Use `combined_train.jsonl` and corresponding valid file."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 11: Evaluate on Statement Rows"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from src.inference.predict import Predictor\n",
"\n",
"# Load new model\n",
"predictor = Predictor(\n",
" model_path=\"models/base/phi3-mini\",\n",
" adapter_path=\"models/adapters/finance-lora-v4\"\n",
")\n",
"\n",
"# Test on statement row\n",
"test_row = \"01-12-2025 | UPI-SWIGGY@ybl | 250.00 | | 45,230.50\"\n",
"prompt = f\"[BANK_STATEMENT] Extract financial entities from this bank statement row:\\n\\n{test_row}\"\n",
"\n",
"result = predictor.predict(email_text=prompt)\n",
"print(f\"📋 Input: {test_row}\")\n",
"print(f\"\\n🎯 Extracted:\")\n",
"print(result.to_json())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## ✅ Phase 2 Checklist\n",
"\n",
"- [ ] Collect bank statements (3-6 months)\n",
"- [ ] Extract text rows using pdfplumber\n",
"- [ ] Manually label 500+ rows\n",
"- [ ] Generate synthetic variations\n",
"- [ ] Add [BANK_STATEMENT] prefix to training\n",
"- [ ] Retrain model\n",
"- [ ] Test accuracy\n",
"\n",
"**Deliverable: Model parses bank statement rows**"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
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"name": "python3"
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