app.py

"""
CCPA Compliance Analyzer - FastAPI server
Uses Ollama (llama3.2:3b or similar) for LLM inference with CCPA RAG context.
Falls back to rule-based analysis if LLM is unavailable.
"""

import os
import json
import re
import logging
from contextlib import asynccontextmanager
from typing import Optional
import httpx

from fastapi import FastAPI
from fastapi.responses import JSONResponse
from pydantic import BaseModel

from ccpa_knowledge import CCPA_SECTIONS

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# ── Config ──────────────────────────────────────────────────────────────────
OLLAMA_HOST = os.getenv("OLLAMA_HOST", "http://localhost:11434")
MODEL_NAME = os.getenv("MODEL_NAME", "llama3.2:3b")

# Full CCPA context for the LLM
CCPA_CONTEXT = "\n\n".join([
    f"**{section}**:\n{text}"
    for section, text in CCPA_SECTIONS.items()
])

SYSTEM_PROMPT = f"""You are a strict CCPA (California Consumer Privacy Act) compliance analyst.
Your job is to analyze business practice descriptions and determine if they violate CCPA law.

Here is the relevant CCPA statute text:

{CCPA_CONTEXT}

Rules:
1. Analyze only against CCPA violations listed above.
2. If the practice clearly violates one or more sections, output harmful=true and list ALL violated sections.
3. If the practice is compliant or unrelated to CCPA, output harmful=false and empty articles list.
4. Be strict: if there is a clear violation, flag it. Do not give benefit of the doubt for clear violations.
5. You MUST respond with ONLY a valid JSON object. No explanation, no markdown, no extra text.

Response format (ONLY THIS, nothing else):
{{"harmful": true, "articles": ["Section 1798.XXX", "Section 1798.YYY"]}}
or
{{"harmful": false, "articles": []}}"""


# ── Lifespan ─────────────────────────────────────────────────────────────────
@asynccontextmanager
async def lifespan(app: FastAPI):
    logger.info("Starting CCPA Compliance Analyzer...")
    # Warm up Ollama connection
    try:
        async with httpx.AsyncClient(timeout=30) as client:
            resp = await client.get(f"{OLLAMA_HOST}/api/tags")
            logger.info(f"Ollama available: {resp.status_code == 200}")
    except Exception as e:
        logger.warning(f"Ollama not available at startup: {e}")
    yield
    logger.info("Shutting down...")


app = FastAPI(title="CCPA Compliance Analyzer", lifespan=lifespan)


# ── Models ───────────────────────────────────────────────────────────────────
class AnalyzeRequest(BaseModel):
    prompt: str

class AnalyzeResponse(BaseModel):
    harmful: bool
    articles: list[str]


# ── Rule-based fallback ───────────────────────────────────────────────────────
def rule_based_analyze(prompt: str) -> dict:
    """Deterministic rule-based CCPA violation detector as fallback."""
    p = prompt.lower()
    found_sections = set()

    # 1798.100 - Undisclosed collection
    if "privacy policy" in p and any(k in p for k in ["doesn't mention", "does not mention", "without mentioning", "not mention"]):
        found_sections.add("Section 1798.100")
    if ("without informing" in p or "without notice" in p) and "collect" in p:
        found_sections.add("Section 1798.100")

    # 1798.105 - Ignoring deletion
    if any(k in p for k in ["ignoring", "ignore", "refusing", "keeping all", "not comply"]):
        if any(k in p for k in ["deletion", "delete", "removal", "request"]):
            found_sections.add("Section 1798.105")

    # 1798.120 - Selling without opt-out / minor consent
    if any(k in p for k in ["selling", "sell", "sharing"]):
        if "without" in p and any(k in p for k in ["opt-out", "opt out", "informing", "notice", "consent"]):
            found_sections.add("Section 1798.120")
            if "without informing" in p or "without notice" in p:
                found_sections.add("Section 1798.100")
        if any(k in p for k in ["14-year", "13-year", "minor", "child", "underage", "under 16", "under 13"]):
            found_sections.add("Section 1798.120")

    # 1798.125 - Discriminatory pricing
    if any(k in p for k in ["higher price", "charge more", "discriminat"]):
        found_sections.add("Section 1798.125")
    if "opted out" in p and any(k in p for k in ["price", "pricing", "charge"]):
        found_sections.add("Section 1798.125")

    # 1798.121 - Sensitive data misuse
    if any(k in p for k in ["sensitive personal information", "biometric data", "precise geolocation"]):
        if any(k in p for k in ["without consent", "without notice", "without informing", "without authorization"]):
            found_sections.add("Section 1798.121")

    harmful = len(found_sections) > 0
    return {"harmful": harmful, "articles": sorted(list(found_sections))}


# ── LLM analysis ──────────────────────────────────────────────────────────────
async def llm_analyze(prompt: str) -> Optional[dict]:
    """Call Ollama LLM for CCPA analysis."""
    payload = {
        "model": MODEL_NAME,
        "messages": [
            {"role": "system", "content": SYSTEM_PROMPT},
            {"role": "user", "content": f"Analyze this business practice for CCPA violations:\n\n{prompt}"}
        ],
        "stream": False,
        "options": {
            "temperature": 0.0,
            "num_predict": 200,
        }
    }
    try:
        async with httpx.AsyncClient(timeout=90) as client:
            resp = await client.post(f"{OLLAMA_HOST}/api/chat", json=payload)
            resp.raise_for_status()
            data = resp.json()
            content = data.get("message", {}).get("content", "")
            logger.info(f"LLM raw response: {content[:200]}")

            # Extract JSON from response
            # Try direct parse
            try:
                result = json.loads(content.strip())
                if "harmful" in result and "articles" in result:
                    return result
            except:
                pass

            # Try regex extraction
            match = re.search(r'\{[^{}]+\}', content, re.DOTALL)
            if match:
                try:
                    result = json.loads(match.group())
                    if "harmful" in result and "articles" in result:
                        return result
                except:
                    pass

            logger.warning("Could not parse LLM response as JSON")
            return None
    except Exception as e:
        logger.warning(f"LLM call failed: {e}")
        return None


# ── Endpoints ─────────────────────────────────────────────────────────────────
@app.get("/health")
async def health():
    return {"status": "ok"}


@app.post("/analyze")
async def analyze(request: AnalyzeRequest):
    logger.info(f"Analyzing: {request.prompt[:100]}")

    # Try LLM first
    result = await llm_analyze(request.prompt)

    if result is None:
        logger.info("Falling back to rule-based analysis")
        result = rule_based_analyze(request.prompt)

    # Ensure correct types
    harmful = bool(result.get("harmful", False))
    articles = list(result.get("articles", []))

    # Enforce logic rules
    if harmful and len(articles) == 0:
        # LLM said harmful but no articles - use rule-based to find articles
        rb = rule_based_analyze(request.prompt)
        if rb["articles"]:
            articles = rb["articles"]
        else:
            # Default to most common violation
            articles = ["Section 1798.100"]

    if not harmful:
        articles = []

    response = {"harmful": harmful, "articles": articles}
    logger.info(f"Result: {response}")
    return JSONResponse(content=response)