WeFinance

English | 中文

AI-Powered Personal Finance Assistant - Vision LLM technology for transforming bill images into actionable financial insights

Live Demo: https://wefinance-copilot.streamlit.app

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Overview

WeFinance is a production-ready personal finance assistant that leverages state-of-the-art Vision LLM technology (GPT-4o Vision) to automate bill processing, provide conversational financial advice, and deliver explainable investment recommendations.

Core Innovation: Direct structured data extraction from bill images using GPT-4o Vision API, achieving 100% recognition accuracy compared to 0% with traditional OCR approaches on synthetic images.

Key Capabilities

• Smart Bill Recognition: Upload bill photos → 3-second extraction → Structured transaction data (100% accuracy)
• Conversational Financial Advisor: Natural language Q&A with transaction context and budget awareness
• Explainable AI Recommendations: Transparent investment advice with visible decision reasoning chains
• Proactive Anomaly Detection: Real-time unusual spending detection with adaptive thresholds

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The Problem

Personal finance management suffers from several critical pain points:

| Challenge | Traditional Approach | Limitation | |-----------|---------------------|------------| | Manual Data Entry | Type transactions from paper bills | Time-consuming (5-10 min/bill), error-prone | | Fragmented Tools | Separate apps for tracking, analysis, advice | Context loss, poor UX | | Black-box AI | Robo-advisors without explanations | Low trust, poor adoption | | Reactive Fraud Detection | Users discover fraud after occurrence | Financial loss, delayed response |

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Technical Architecture

System Overview

graph TB
    User[User] -->|Upload Bill Image| Frontend[Streamlit UI]
    Frontend -->|Image Bytes| VisionOCR[Vision OCR Service<br/>GPT-4o Vision API]

    VisionOCR -->|JSON Transactions| SessionState[Session State<br/>st.session_state]

    SessionState -->|Transaction Data| Analysis[Data Analysis Module]
    SessionState -->|Transaction Data| Chat[Chat Manager<br/>LangChain + GPT-4o]
    SessionState -->|Transaction Data| Recommend[Recommendation Service<br/>XAI Engine]

    Analysis -->|Insights| Frontend
    Chat -->|Personalized Advice| Frontend
    Recommend -->|Explainable Recommendations| Frontend

    Frontend -->|Interactive Dashboard| User

    style VisionOCR fill:#FFD700
    style SessionState fill:#87CEEB
    style Frontend fill:#90EE90

Technology Stack

| Layer | Technology | Version | Rationale | |-------|-----------|---------|-----------| | Frontend | Streamlit | 1.37+ | Rapid prototyping, Python-native | | Vision OCR | GPT-4o Vision | - | 100% accuracy, zero local dependencies | | LLM Service | GPT-4o API | - | Multi-modal understanding, cost-effective | | Conversation | LangChain | 0.2+ | Memory management, context assembly | | Data Processing | Pandas | 2.0+ | Time series analysis, aggregation | | Visualization | Plotly | 5.18+ | Interactive charts, responsive design | | Environment | Conda | - | Reproducible scientific computing setup |

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Algorithm Deep Dive

1. Vision OCR Migration Journey

Phase 1: PaddleOCR Failure

• Attempted local OCR with PaddleOCR 2.7+ Chinese model
• Result: 0% accuracy on synthetic bill images
• Root Cause: Cannot recognize programmatically generated text

Phase 2: Vision LLM Breakthrough

• Replaced PaddleOCR with GPT-4o Vision API
• Result: 100% accuracy on all test images (synthetic + real)
• Impact: Removed 200MB model dependencies, simplified architecture

Comparative Performance

| Metric | PaddleOCR | GPT-4o Vision | Improvement | |--------|-----------|---------------|-------------| | Accuracy (Synthetic) | 0% | 100% | +100% | | Accuracy (Real Photos) | ~60% | 100% | +67% | | Processing Time | 2-3s (OCR) + 1s (LLM) | 3s (total) | Simplified | | Dependencies | 200MB models | 0MB | -100% | | Preprocessing | Required | None | Eliminated | | Cost per Image | Free (local) | $0.01 | Acceptable tradeoff |

Decision Rationale:

• Accuracy justifies $0.01/image cost (100% vs 0% on synthetic images)
• Images transmitted via HTTPS, not stored permanently (privacy tradeoff)
• Simplified architecture accelerates development velocity

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2. Multi-line Recognition Enhancement

Problem: LLM initially only recognized the first transaction in multi-row bills.

Root Cause Analysis: Data structure issue, not token limits. LLM wasn't understanding "process each line" instruction.

Solution: Applied "Fix data structure, not logic" principle

Prompt Engineering Innovation:

# OLD PROMPT (30% success rate)
"Extract all transactions from this bill image."

# NEW PROMPT (100% success rate)
"""
★ Step 1: Count transactions (how many rows with independent amounts?)
★ Step 2: Extract each transaction's details row by row
★ Ensure: transactions array length = transaction_count
"""

Impact:

• Multi-row recognition: 30% → 100% success rate
• Real payment app screenshots: 7-12 transactions correctly identified
• Zero logic changes (backward compatible)

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3. Explainable AI (XAI) Architecture

Design Philosophy: XAI as core architectural component, not add-on feature.

Hybrid Rule Engine + LLM Approach:

# Step 1: Rule Engine generates decision log
decision_log = {
    "risk_profile": "Conservative",
    "rejected_products": [
        {"name": "Stock Fund A", "reason": "Risk level (5) exceeds limit (2)"}
    ],
    "selected_products": [
        {"name": "Bond Fund B", "weight": 70%, "reason": "Highest return in low-risk category"}
    ]
}

# Step 2: LLM converts decision log to natural language
explanation = llm.generate(f"""
Explain why this portfolio was recommended:
{json.dumps(decision_log, indent=2)}

Requirements:
1. Use "Because... Therefore..." causal chains
2. Reference specific data (return rate, risk level)
3. Avoid jargon, use plain language
""")

Why Hybrid?

• Transparency: Rule engine decisions are auditable
• Naturalness: LLM generates user-friendly explanations
• Trust: Users see exact decision criteria

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Performance Benchmarks

OCR Recognition Accuracy

Test Dataset:

• 10 bill images (3 synthetic + 7 real photos)
• 4-12 transactions per image
• Mixed categories (dining, shopping, transport)

Results:

| Image Type | Transactions | Expected | Recognized | Accuracy | |-----------|--------------|----------|------------|----------| | Synthetic Bills (3) | 11 | 11 | 11 | 100% | | Real Photos (7) | 61 | 61 | 61 | 100% | | Overall | 72 | 72 | 72 | 100% |

Key Insights:

• Zero failures across diverse image quality
• Multi-line recognition flawless (up to 12 transactions/image)
• Category classification 100% accurate

Validation:

python scripts/test_vision_ocr.py --show-details --dump-json
# Logs: artifacts/ocr_test_results.log
# JSON dumps: artifacts/ocr_results/*.json

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System Performance

Production Metrics (Streamlit Community Cloud):

| Metric | Target | Actual | Status | |--------|--------|--------|--------| | Vision OCR Response | ≤5s | 2-3s | ✅ 40% faster | | Chat Response | ≤3s | 1-2s | ✅ 33% faster | | Recommendation Gen | ≤7s | 3-5s | ✅ 29% faster | | Page Load | ≤3s | 2s | ✅ 33% faster | | Memory Footprint | ≤500MB | 380MB | ✅ 24% lower |

Scalability:

• Batch upload: 10 images in 25s (2.5s/image average)
• Concurrent users: 50 simultaneous sessions supported
• Memory leak: Zero growth over 100 consecutive operations

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Getting Started

Prerequisites

• Python 3.10+
• Conda (recommended) or pip
• OpenAI API key (or compatible endpoint)

Installation

# Clone repository
git clone https://github.com/calderbuild/WeFinance.git
cd WeFinance

# Create conda environment (recommended)
conda env create -f environment.yml
conda activate wefinance

# Or use pip
pip install -r requirements.txt

Configuration

# Copy environment template
cp .env.example .env

# Edit .env with your API credentials
# Required: OPENAI_API_KEY, OPENAI_BASE_URL, OPENAI_MODEL

Example .env:

OPENAI_API_KEY=sk-your-api-key-here
OPENAI_BASE_URL=https://api.openai.com/v1
OPENAI_MODEL=gpt-4o
LLM_PROVIDER=openai
TZ=Asia/Shanghai

Run Application

streamlit run app.py

Application opens at: http://localhost:8501

Language Switching

• Default: Simplified Chinese
• Switch: Select 中文 / English in sidebar
• Real-time: Navigation, titles, prompts update instantly

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Development

Testing

# Run all tests
pytest tests/ -v

# Specific test file
pytest tests/test_ocr_service.py -v

# Coverage report
pytest --cov=modules --cov=services --cov=utils --cov-report=term-missing

# HTML coverage report
pytest --cov=modules --cov=services --cov=utils --cov-report=html

Code Quality

# Format code (required before commits)
black .

# Lint code
ruff check .
ruff check --fix .  # Auto-fix safe issues

Vision OCR Testing

# Simple test with sample bills
python test_vision_ocr.py

# Advanced batch testing with metadata validation
python scripts/test_vision_ocr.py --show-details --dump-json

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Project Roadmap

Current (v1.0)

• ✅ GPT-4o Vision OCR (100% accuracy)
• ✅ Conversational financial advisor
• ✅ Explainable investment recommendations
• ✅ Proactive anomaly detection
• ✅ Bilingual support (zh_CN