Spaces:
Sleeping
Sleeping
File size: 1,868 Bytes
4bdde62 2ff9250 | 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 | from __future__ import annotations
from typing import Optional
import tensorflow as tf
import os
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
MODEL_DIR_DEFAULT = os.path.join(BASE_DIR, 'data', 'model', 'sentiment_cnn')
# Cache di processo (il modello viene caricato una volta sola)
_INFER: Optional[tf.types.experimental.ConcreteFunction] = None
_MODEL_DIR_LOADED: Optional[str] = None
def _load_infer(model_dir: str) -> tf.types.experimental.ConcreteFunction:
"""
Load SavedModel once and return serving function.
TF 2.12 compatible, expects signature 'serving_default'.
"""
sm = tf.saved_model.load(model_dir)
return sm.signatures["serving_default"]
def _get_infer(model_dir: str) -> tf.types.experimental.ConcreteFunction:
global _INFER, _MODEL_DIR_LOADED
model_dir = str(model_dir)
if _INFER is None or _MODEL_DIR_LOADED != model_dir:
_INFER = _load_infer(model_dir)
_MODEL_DIR_LOADED = model_dir
return _INFER
def _predict_prob_positive(text: str, model_dir: str) -> float:
infer = _get_infer(model_dir)
x = tf.constant([text], dtype=tf.string)
out = infer(text=x)
# Output key stabilized by our exporter
y = out["prob"]
return float(y.numpy()[0][0])
def binary_classification(text: str):
"""
IDENTICAL behavior to legacy:
- if text is empty -> ({"error": "Sentence is required"}, 415)
- else -> {"positive": "0.xx", "negative": "0.yy"} (strings, 2 decimals)
"""
try:
if text == "":
raise Exception
except:
return {"error": "Sentence is required"}, 415
prob_pos = _predict_prob_positive(text, MODEL_DIR_DEFAULT)
positive = prob_pos
negative = 1.0 - positive
labels = {
"positive": f"{positive:.2f}",
"negative": f"{negative:.2f}",
}
return labels
|