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"""
Sentiment analysis module using Hugging Face Inference API to avoid local model downloads.
"""
import os
import hashlib
import logging
from functools import lru_cache
import httpx
# Environment variables (set HF_API_TOKEN in your Space's Settings)
HF_API_TOKEN = os.getenv("HF_API_TOKEN", "")
API_URL = "https://api-inference.huggingface.co/models/distilbert-base-uncased-finetuned-sst-2-english"
HEADERS = {"Authorization": f"Bearer {HF_API_TOKEN}"}
# In-memory cache for latest sentiment
class SentimentCache:
latest_id: int = 0
latest_result: dict = {}
@classmethod
def _hash(cls, text: str) -> str:
return hashlib.sha256(text.encode()).hexdigest()
@classmethod
@lru_cache(maxsize=128)
def _analyze(cls, text: str):
try:
response = httpx.post(API_URL, headers=HEADERS, json={"inputs": text}, timeout=20)
response.raise_for_status()
data = response.json()
# Expecting list of {label, score}
if isinstance(data, list) and data:
return data[0]
raise ValueError("Unexpected response format: %s" % data)
except Exception as e:
logging.error("❌ Sentiment API error: %s", e)
return {"label": "ERROR", "score": 0.0}
@classmethod
def compute(cls, text: str):
"""Trigger sentiment inference via API and update latest result."""
res = cls._analyze(text)
cls.latest_id += 1
cls.latest_result = {
"text": text,
"label": res.get("label"),
"score": round(res.get("score", 0.0), 4)
}
logging.info("✅ Sentiment computed: %s", cls.latest_result)
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