ankushka/projectankraj

CCPA Compliance Analyzer — OPEN HACK 2026

Solution Overview

This system analyzes natural-language business practice descriptions and determines whether they violate the California Consumer Privacy Act (CCPA). It uses a RAG (Retrieval-Augmented Generation) architecture:

1. Knowledge Base: The full CCPA statute (key sections 1798.100–1798.135) is pre-encoded as structured text in ccpa_knowledge.py, covering all major consumer rights and business obligations.
2. LLM Inference: Llama 3.2 3B (via Ollama) receives a system prompt containing the CCPA statute context + the user's business practice prompt and returns a JSON classification.
3. Rule-Based Fallback: A deterministic keyword/pattern matcher provides a reliable backup if the LLM is unavailable or returns unparseable output.
4. FastAPI Server: Exposes GET /health and POST /analyze endpoints on port 8000.

Pipeline: POST /analyze → LLM (Llama 3.2 3B via Ollama) with CCPA context → JSON parse → logic validation → {"harmful": bool, "articles": [...]}

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Docker Run Command

docker run --gpus all -p 8000:8000 -e HF_TOKEN=xxx yourusername/ccpa-compliance:latest

Without GPU (CPU-only mode, slower):

docker run -p 8000:8000 yourusername/ccpa-compliance:latest

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Environment Variables

Variable	Required	Description
HF_TOKEN	No	Hugging Face access token (not needed for llama3.2 via Ollama)
MODEL_NAME	No	Ollama model to use (default: llama3.2:3b)
OLLAMA_HOST	No	Ollama server URL (default: http://localhost:11434)

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GPU Requirements

• Recommended: NVIDIA GPU with ≥4GB VRAM (RTX 3060 or better)
• CPU-only fallback: Supported, but inference will be significantly slower (~30-60s per request). The 120s timeout in the test script provides sufficient buffer.
• Model size: llama3.2:3b is ~2GB on disk, ~2GB VRAM when loaded

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Local Setup Instructions (Fallback — no Docker)

Use only if Docker fails. Manual deployment incurs a score penalty.

Requirements: Linux, Python 3.11+, Ollama

# 1. Install Ollama
curl -fsSL https://ollama.com/install.sh | sh

# 2. Start Ollama and pull model
ollama serve &
ollama pull llama3.2:3b

# 3. Install Python dependencies
pip install fastapi uvicorn httpx pydantic

# 4. Run the FastAPI server
cd /path/to/ccpa_project
uvicorn app:app --host 0.0.0.0 --port 8000

# 5. Verify it's running
curl http://localhost:8000/health

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API Usage Examples

Health Check

curl http://localhost:8000/health
# Response: {"status": "ok"}

Analyze — Violation Detected

curl -X POST http://localhost:8000/analyze \
  -H "Content-Type: application/json" \
  -d '{"prompt": "We are selling our customers personal information to data brokers without giving them a chance to opt out."}'

# Response:
# {"harmful": true, "articles": ["Section 1798.120", "Section 1798.100"]}

Analyze — No Violation

curl -X POST http://localhost:8000/analyze \
  -H "Content-Type: application/json" \
  -d '{"prompt": "We provide a clear privacy policy and allow customers to opt out of data selling at any time."}'

# Response:
# {"harmful": false, "articles": []}

Using docker-compose (with organizer test script)

docker compose up -d
python validate_format.py
docker compose down

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Project Structure

ccpa_project/
├── app.py              # FastAPI server + LLM/rule-based analysis
├── ccpa_knowledge.py   # CCPA statute knowledge base (RAG source)
├── requirements.txt    # Python dependencies
├── Dockerfile          # Container definition (pre-pulls llama3.2:3b)
├── start.sh            # Container startup (starts Ollama + uvicorn)
├── docker-compose.yml  # Compose config for easy orchestration
└── README.md           # This file

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Notes on Accuracy

• The system cites sections based on CCPA statute analysis, not keyword matching alone.
• The LLM is instructed to identify all violated sections, not just the most obvious one.
• The rule-based fallback provides reliable detection for common violation patterns.
• Incorrect article citations result in zero marks per the scoring rubric, so the system is conservative: it only cites a section when there is clear evidence of a violation matching that section's specific requirements.