File size: 7,424 Bytes
fc329a3 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 | """Cache zero-shot API emotion scores for SemEval-2007 Affective Text."""
from __future__ import annotations
import argparse
import json
import logging
import os
import re
import time
import urllib.error
import urllib.parse
import urllib.request
from pathlib import Path
import sys
sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
from src.data import EMOTION_NAMES, load_affective_text, load_prediction_cache
logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
log = logging.getLogger(__name__)
PROMPT_TEMPLATE = (
'Rate the following news headline on 6 emotions: anger, disgust, fear, joy, sadness, surprise. '
'Return only 6 numbers from 0 to 100, comma-separated, in that order.\n'
'Headline: "{headline}"\n'
"Scores:"
)
def parse_scores(text: str) -> list[float]:
nums = re.findall(r"-?\d+(?:\.\d+)?", text)
if len(nums) < 6:
raise ValueError(f"Could not parse 6 scores from response: {text!r}")
scores = [max(float(x), 0.0) for x in nums[:6]]
if sum(scores) <= 0:
raise ValueError(f"Parsed zero-sum scores from response: {text!r}")
return scores
def call_openai_chat_completions(
headline: str,
model: str,
api_key: str,
base_url: str,
timeout_sec: float,
) -> tuple[str, dict]:
prompt = PROMPT_TEMPLATE.format(headline=headline)
payload = {
"model": model,
"messages": [
{"role": "system", "content": "You are a precise annotation model."},
{"role": "user", "content": prompt},
],
"temperature": 0,
}
req = urllib.request.Request(
url=base_url.rstrip("/") + "/chat/completions",
data=json.dumps(payload).encode("utf-8"),
headers={
"Content-Type": "application/json",
"Authorization": f"Bearer {api_key}",
},
method="POST",
)
with urllib.request.urlopen(req, timeout=timeout_sec) as resp:
body = json.loads(resp.read().decode("utf-8"))
text = body["choices"][0]["message"]["content"]
return text, body
def call_gemini_generate_content(
headline: str,
model: str,
api_key: str,
base_url: str,
timeout_sec: float,
) -> tuple[str, dict]:
prompt = PROMPT_TEMPLATE.format(headline=headline)
payload = {
"contents": [
{
"role": "user",
"parts": [{"text": prompt}],
}
],
"generationConfig": {
"temperature": 0,
},
}
url = (
base_url.rstrip("/")
+ f"/models/{model}:generateContent?key={urllib.parse.quote(api_key)}"
)
req = urllib.request.Request(
url=url,
data=json.dumps(payload).encode("utf-8"),
headers={"Content-Type": "application/json"},
method="POST",
)
with urllib.request.urlopen(req, timeout=timeout_sec) as resp:
body = json.loads(resp.read().decode("utf-8"))
candidates = body.get("candidates", [])
if not candidates:
raise KeyError(f"No Gemini candidates in response: {body}")
parts = candidates[0].get("content", {}).get("parts", [])
text = "\n".join(part.get("text", "") for part in parts if part.get("text"))
if not text:
raise KeyError(f"No text parts in Gemini response: {body}")
return text, body
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--data-dir", default="data/raw/AffectiveText.Semeval.2007")
parser.add_argument("--output", default="data/processed/affective_text_predictions.jsonl")
parser.add_argument("--provider", choices=["openai", "gemini"], default="gemini")
parser.add_argument("--model", default=None)
parser.add_argument("--base-url", default=None)
parser.add_argument("--api-key-env", default=None)
parser.add_argument("--limit", type=int, default=None)
parser.add_argument("--sleep-sec", type=float, default=0.0)
parser.add_argument("--timeout-sec", type=float, default=60.0)
parser.add_argument("--overwrite", action="store_true")
args = parser.parse_args()
if args.model is None:
if args.provider == "gemini":
args.model = os.environ.get("GEMINI_MODEL", "gemini-2.0-flash-001")
else:
args.model = os.environ.get("OPENAI_MODEL", "gpt-4o-mini-2024-07-18")
if args.base_url is None:
if args.provider == "gemini":
args.base_url = os.environ.get("GEMINI_BASE_URL", "https://generativelanguage.googleapis.com/v1beta")
else:
args.base_url = os.environ.get("OPENAI_BASE_URL", "https://api.openai.com/v1")
if args.api_key_env is None:
args.api_key_env = "GEMINI_API_KEY" if args.provider == "gemini" else "OPENAI_API_KEY"
api_key = os.environ.get(args.api_key_env)
if not api_key:
raise EnvironmentError(f"Missing API key in env var {args.api_key_env}")
data = load_affective_text(args.data_dir)
ids = data["ids"]
headlines = data["headlines"]
if args.limit is not None:
ids = ids[:args.limit]
headlines = headlines[:args.limit]
out_path = Path(args.output)
out_path.parent.mkdir(parents=True, exist_ok=True)
existing = {}
if out_path.exists() and not args.overwrite:
existing = load_prediction_cache(out_path)
log.info(f"Loaded {len(existing)} cached predictions from {out_path}")
n_done = 0
with open(out_path, "a" if existing and not args.overwrite else "w", encoding="utf-8") as f:
for idx, headline in zip(ids, headlines):
if idx in existing and not args.overwrite:
continue
try:
if args.provider == "gemini":
raw_text, raw_json = call_gemini_generate_content(
headline=headline,
model=args.model,
api_key=api_key,
base_url=args.base_url,
timeout_sec=args.timeout_sec,
)
else:
raw_text, raw_json = call_openai_chat_completions(
headline=headline,
model=args.model,
api_key=api_key,
base_url=args.base_url,
timeout_sec=args.timeout_sec,
)
scores = parse_scores(raw_text)
except (urllib.error.URLError, urllib.error.HTTPError, ValueError, KeyError) as exc:
log.error(f"Failed on id={idx}: {exc}")
continue
row = {
"id": idx,
"headline": headline,
"emotions": EMOTION_NAMES,
"scores": scores,
"provider": args.provider,
"model": args.model,
"base_url": args.base_url,
"prompt_template": PROMPT_TEMPLATE,
"raw_text": raw_text,
"raw_response": raw_json,
}
f.write(json.dumps(row, ensure_ascii=True) + "\n")
f.flush()
n_done += 1
if n_done % 50 == 0:
log.info(f"Cached {n_done} new predictions")
if args.sleep_sec > 0:
time.sleep(args.sleep_sec)
log.info(f"Finished. Predictions cached at {out_path}")
if __name__ == "__main__":
main()
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