Spaces:
Sleeping
Sleeping
File size: 6,693 Bytes
26bf1c9 28f702f 26bf1c9 28f702f 26bf1c9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 | """
FastAPI application for CounterFeint — a multi-agent ad-fraud FraudArena.
Creates the OpenEnv server via create_app() (Round-1-compatible `/ws`)
and layers on Round-2 role-specific routes:
/ws/fraudster Fraudster agent (proposes / modifies ads)
/ws/investigator Investigator agent (reviews ads, renders verdicts)
/ws/auditor Auditor agent (audits Investigator + Fraudster traces)
Custom HTTP endpoints: /tasks, /baseline, /grader, /matches.
Gradio is disabled (ENABLE_WEB_INTERFACE=false); the HTML UI lives at /investigate.
"""
from __future__ import annotations
import json
import logging
import os
from pathlib import Path
from typing import Any, Dict
from openenv.core.env_server import create_app
try:
from ..models import (
AdReviewAction,
AdReviewObservation,
AuditorAction,
AuditorObservation,
FraudsterAction,
FraudsterObservation,
)
except ImportError:
from models import (
AdReviewAction,
AdReviewObservation,
AuditorAction,
AuditorObservation,
FraudsterAction,
FraudsterObservation,
)
from .environment import AdFraudEnvironment, get_last_grader_result
from .arena_ui import register_arena_ui
from .investigate_ui import register_investigate_ui
from .multi_agent_ws import register_multi_agent_routes
from .public_api import register_public_api
from .referee import get_last_grader_result as get_last_multi_agent_grader_result
logger = logging.getLogger(__name__)
# Do not mount OpenEnv's Gradio stack (single FastAPI process on port 8000).
os.environ.setdefault("ENABLE_WEB_INTERFACE", "false")
app = create_app(
AdFraudEnvironment,
AdReviewAction,
AdReviewObservation,
env_name="counterfeint",
)
register_investigate_ui(app)
register_multi_agent_routes(app)
register_public_api(app)
register_arena_ui(app)
# ------------------------------------------------------------------
# Custom endpoints required by the competition
# ------------------------------------------------------------------
@app.get("/tasks", tags=["Competition"])
async def tasks() -> Dict[str, Any]:
"""Return the list of tasks, the action schema, and the R2 role catalog."""
try:
from ..data.ad_generator import TASK_CONFIGS
except ImportError:
from data.ad_generator import TASK_CONFIGS
task_list = []
for tid, cfg in TASK_CONFIGS.items():
task_list.append({
"id": cfg.task_id,
"name": cfg.name,
"difficulty": cfg.difficulty,
"queue_size": cfg.queue_size,
"action_budget": cfg.action_budget,
"description": cfg.description,
})
roles = {
"fraudster": {
"description": (
"Adversarial agent. Proposes and mutates ads into the shared "
"queue during its turn, reacting to Investigator feedback."
),
"ws": "/ws/fraudster",
"action_schema": FraudsterAction.model_json_schema(),
"observation_schema": FraudsterObservation.model_json_schema(),
},
"investigator": {
"description": (
"Review agent. Investigates ads via sub-tools and renders "
"verdicts (approve/reject/escalate). Cannot see Fraudster "
"intent — only the growing queue."
),
"ws": "/ws/investigator",
"action_schema": AdReviewAction.model_json_schema(),
"observation_schema": AdReviewObservation.model_json_schema(),
},
"auditor": {
"description": (
"Third-agent arbiter. After the match ends, audits the "
"Investigator's reasoning (Track A) and the Fraudster's "
"ad plausibility (Track B). Emits flags + a final audit report."
),
"ws": "/ws/auditor",
"action_schema": AuditorAction.model_json_schema(),
"observation_schema": AuditorObservation.model_json_schema(),
},
}
return {
"tasks": task_list,
"action_schema": AdReviewAction.model_json_schema(),
"roles": roles,
"multi_agent_endpoints": {
"fraudster_ws": "/ws/fraudster",
"investigator_ws": "/ws/investigator",
"auditor_ws": "/ws/auditor",
"matches": "/matches",
"grader": "/grader",
},
}
@app.get("/baseline", tags=["Competition"])
async def baseline() -> Dict[str, Any]:
"""Return baseline scores, running live inference if credentials are available."""
baseline_path = Path(__file__).resolve().parent.parent / "baseline_scores.json"
has_creds = all(os.getenv(v) for v in ("API_BASE_URL", "MODEL_NAME", "HF_TOKEN"))
if has_creds:
try:
try:
from ..inference import run_baseline
except ImportError:
from inference import run_baseline
scores = run_baseline()
with open(baseline_path, "w") as f:
json.dump(scores, f, indent=2)
return scores
except Exception as e:
logger.warning("Live baseline failed, falling back to cached: %s", e)
if baseline_path.exists():
with open(baseline_path) as f:
return json.load(f)
return {
"error": "No baseline scores available. Set API_BASE_URL, MODEL_NAME, and HF_TOKEN to run live inference.",
"tasks": {},
}
@app.get("/grader", tags=["Competition"])
async def grader() -> Dict[str, Any]:
"""
Return grader score from the most recently completed episode. Prefers
a multi-agent (Referee) result if one exists; falls back to the R1
Investigator result otherwise.
"""
multi_agent_result = get_last_multi_agent_grader_result()
if multi_agent_result and multi_agent_result.get("grader_score") is not None:
multi_agent_result.setdefault("mode", "multi_agent")
return multi_agent_result
result = get_last_grader_result()
if not result:
return {
"error": "No completed episode. Run an episode via WebSocket or the /investigate UI.",
"grader_score": None,
}
result.setdefault("mode", "single_agent")
return result
def main() -> None:
"""Entry point for direct execution."""
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=8000)
if __name__ == "__main__":
main()
|