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import logging
from fastapi import FastAPI
from pydantic import BaseModel
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
# ------------------------------
# Logging setup
# ------------------------------
logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
logger = logging.getLogger(__name__)
logger.info("🚀 Starting Privacy Audit AI Backend...")
# ------------------------------
# Load model and tokenizer
# ------------------------------
tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-small")
model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-small")
# ------------------------------
# FastAPI app
# ------------------------------
app = FastAPI(title="Privacy Audit AI", version="1.0.0")
# ------------------------------
# Request model
# ------------------------------
class AnalyzeInput(BaseModel):
os_apps: list[str] = []
browser_extensions: list[str] = []
account_apps: list[str] = []
# ------------------------------
# Utility functions
# ------------------------------
def generate_plain_text(input_text: str):
"""Generate plain-language explanation using the model."""
logger.info(f"Generating explanation for input: {input_text[:60]}...")
inputs = tokenizer(f"Explain privacy risks in plain language:\n{input_text}", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=150)
explanation = tokenizer.decode(outputs[0], skip_special_tokens=True)
return explanation
def explain_risks(os_apps, browser_exts, account_apps):
summary_text = (
f"OS Apps: {', '.join(os_apps[:10])}, "
f"Browser Extensions: {', '.join(browser_exts[:5])}, "
f"Connected Apps: {', '.join(account_apps)}"
)
return generate_plain_text(summary_text)
# ------------------------------
# Endpoints
# ------------------------------
@app.get("/ping")
def ping():
return {"status": "ok", "message": "Backend is alive!"}
@app.post("/analyze")
def analyze(data: AnalyzeInput):
explanation = explain_risks(data.os_apps, data.browser_extensions, data.account_apps)
return {"plain_language": explanation}
@app.get("/audit")
def audit_mvp():
findings = "App X has camera access, App Y has location access, Chrome has 5 extensions"
explanation = generate_plain_text(findings)
return {
"findings": findings,
"plain_language": explanation,
"risk_level": "Medium"
}
@app.post("/audit")
def audit_mvp_post(data: AnalyzeInput):
findings_text = (
f"Detected {len(data.os_apps)} installed apps, "
f"{len(data.browser_extensions)} browser extensions, and "
f"{len(data.account_apps)} connected account apps."
)
explanation = generate_plain_text(findings_text)
return {
"findings": findings_text,
"plain_language": explanation,
"risk_level": "Medium"
}
uvicorn app:app --host 0.0.0.0 --port 7860
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