Merge pull request #6 from pie-314/fix/grading-bugs-and-cli

Fix pipeline 0.0 scoring override, resolve test floating-point flakiness, and add readable CLI output for inference.py

Updated api base url

Merge pull request #4 from subhdotsol/fix

Updated readme

Merge pull request #3 from subhdotsol/fix

small fixes

Merge pull request #2 from pie-314/main

added graders and rewards

Updated readme

Merge pull request #1 from subhdotsol/feat/ai-integration

rewrite README with full setup guide and integration contracts

update env example to use Groq instead of HuggingFace

document all LLM prompts — what they do and why they're designed that way

add 59 tests for the LLM modules — all run offline, no API needed

wire up the main LLM pipeline — defender + classifiers in one place

add defense scorer — how well did the defender actually handle that?

add attack classifier — did the attacker actually win this turn?

add defender LLM — the AI that holds the line against attacks

add ConversationManager to give the defender a memory

add Groq client setup — one place to rule all API calls

chore: final cleanup — remove stray files, verify all imports resolve

refactor: replace datetime.utcnow() with timezone-aware datetime.now(UTC)

docs: add example usage code snippet for httpx client in README

docs: add task difficulty table and Docker usage section to README

docs: add API endpoint table to README

test(graders): add programmatic_grader tests — structure, score range, letter grade

test(graders): add hard_grader tests — breakthrough bonus and repetitive penalty

test(graders): add medium_grader tests — diversity scores higher than uniform strategies

test(graders): add easy_grader tests — empty, good, bad episode and range check

test(environment): add injection tests — custom reward_computer and llm_pipeline are called

test(environment): add framing sanitization tests for script and javascript injection

test(environment): add guard tests — step without reset raises, step past max_turns raises

test(environment): add step() tests — turn increment, history append, result shape

test(environment): add reset() tests — observation shape, history clear, unique ids

chore(tests): add tests package __init__.py

feat(inference): add main() — runs all 3 task difficulties with 20-minute guard

feat(inference): implement run_episode() — full reset→step loop with httpx client

feat(inference): add strategy escalation logic — harder strategies as turns increase

feat(inference): add generate_attack() — uses OpenAI client to craft next framing

feat(inference): scaffold inference.py with OpenAI client config and env vars

feat(llm): add run_llm_pipeline() stub with correct return shape for Person 3

chore(llm): add llm package __init__.py

feat(rewards): add RewardComputer wrapper class with __call__ and reset() for injection

feat(rewards): add reset_scorer() stub for novelty scorer reset between episodes

feat(rewards): add compute_rewards() stub with correct return shape for Person 2

chore(rewards): add rewards package __init__.py

feat(graders): add letter grade, summary and full metrics dict to grade_episode()

feat(graders): implement all 5 scoring components in programmatic_grader

feat(graders): add programmatic_grader scaffold and grade_episode() signature