fix: handle null user_id in session middleware
refactor: extract auth helper to shared module
feat: add retry logic to payment webhook handler

- Fixed null handling in session middleware
- Refactored authentication helpers
- Added retry logic to payment webhook

import dspy
import vizpy
 
dspy.configure(lm=dspy.LM("openai/gpt-4o-mini"))
 
 
class ChangelogEntry(dspy.Signature):
    """Translate a commit message into a user-facing changelog entry.
    Write from the user's perspective: what changed for them, not what the code does."""
 
    commit: str = dspy.InputField(desc="Git commit message with type prefix (fix/feat/refactor)")
    context: str = dspy.InputField(desc="Brief description of the affected feature area")
    entry: str = dspy.OutputField(
        desc="One sentence changelog entry. Start with the user impact, not the technical mechanism."
    )
 
 
module = dspy.Predict(ChangelogEntry)
 
 
_train_data = [
    {
        "commit": "fix: handle null user_id in session middleware",
        "context": "Checkout flow — affects users whose session expired mid-purchase",
        "good_entry": "Fixed a crash that could occur when your session expired during checkout.",
        "bad_pattern": "technical mechanism",
        "must_avoid": ["null", "middleware", "session middleware", "user_id"],
        "must_convey": "checkout crash fixed",
    },
    {
        "commit": "feat: add retry logic to payment webhook handler",
        "context": "Subscription billing — occasional failed payment notifications",
        "good_entry": "Subscriptions now recover automatically from temporary payment failures instead of requiring manual renewal.",
        "bad_pattern": "implementation detail",
        "must_avoid": ["retry logic", "webhook", "handler"],
        "must_convey": "subscription recovery",
    },
    {
        "commit": "fix: prevent race condition in concurrent file uploads",
        "context": "Document upload — affected users uploading multiple files simultaneously",
        "good_entry": "Fixed a bug where uploading multiple files at once could result in missing or duplicate files.",
        "bad_pattern": "technical jargon",
        "must_avoid": ["race condition", "concurrent", "thread"],
        "must_convey": "multiple file upload",
    },
    {
        "commit": "feat: implement exponential backoff for API rate limiting",
        "context": "Integrations — affects users who hit API limits on high-volume plans",
        "good_entry": "API integrations now handle rate limits gracefully instead of failing with an error.",
        "bad_pattern": "implementation",
        "must_avoid": ["exponential backoff", "rate limiting", "implement"],
        "must_convey": "API integration improvement",
    },
    {
        "commit": "fix: sanitize HTML in user-submitted comments",
        "context": "Community forums — potential display issues with special characters",
        "good_entry": "Fixed comment formatting issues caused by special characters like < and >.",
        "bad_pattern": "security terminology",
        "must_avoid": ["sanitize", "HTML", "XSS"],
        "must_convey": "comment display fix",
    },
    {
        "commit": "refactor: migrate auth token storage from localStorage to httpOnly cookies",
        "context": "Account security — background security improvement",
        "good_entry": "Improved account security by updating how login sessions are stored.",
        "bad_pattern": "implementation detail",
        "must_avoid": ["localStorage", "httpOnly", "cookie", "token storage", "refactor"],
        "must_convey": "security improvement",
    },
]
 
 
def metric(example, prediction):
    entry = prediction.entry.lower()
 
    # Check for technical jargon leaking through
    leaked_jargon = [term for term in example["must_avoid"] if term.lower() in entry]
 
    # Rough check that the user-facing concept is present
    convey_words = example["must_convey"].lower().split()
    conveys_impact = sum(1 for w in convey_words if w in entry) >= len(convey_words) * 0.5
 
    if not leaked_jargon and conveys_impact:
        score, success = 1.0, True
        feedback = ""
    elif leaked_jargon and conveys_impact:
        score, success = 0.5, False
        feedback = (
            f"The entry leaks technical terms: {', '.join(leaked_jargon)}. "
            f"Rewrite from the user's perspective — what changed for them, not what the code does. "
            f"Example of the right register: '{example['good_entry']}'"
        )
    else:
        score, success = 0.0, False
        feedback = (
            f"Entry doesn't convey the user impact ('{example['must_convey']}'). "
            f"Avoid: {', '.join(leaked_jargon or ['technical implementation language'])}. "
            f"Target register: '{example['good_entry']}'"
        )
 
    return vizpy.Score(
        value=score,
        is_success=success,
        feedback=feedback,
        error_type="jargon_leak" if leaked_jargon else ("missing_impact" if not conveys_impact else ""),
    )
 
 
optimizer = vizpy.ContraPromptOptimizer(metric=metric)
optimized = optimizer.optimize(module, _train_data)
 
 
# Test
result = optimized(
    commit="fix: resolve deadlock in database connection pool under high concurrency",
    context="Affects users on high-traffic plans who experienced random request failures",
)
print(result.entry)
# "Fixed intermittent request failures that affected high-traffic accounts during peak usage."