Spaces:
Runtime error
Runtime error
File size: 1,318 Bytes
bce23e4 1031728 bce23e4 7deafbb d810840 7deafbb 9f6db74 |
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 |
from keras.preprocessing.sequence import pad_sequences
import numpy as np
import re
# import tensorflow as tf
import os
import requests
from keras.models import load_model
headers = {"Authorization": f"Bearer {os.environ['HF_Token']}"}
model = load_model("./RNN_model.keras")
def query_embeddings(texts):
payload = {"inputs": texts, "options": {"wait_for_model": True}}
model_id = "sentence-transformers/sentence-t5-base"
API_URL = (
f"https://api-inference.huggingface.co/pipeline/feature-extraction/{model_id}"
)
response = requests.post(API_URL, headers=headers, json=payload)
return response.json()
def preprocess(sentences):
max_len = 1682
embeddings = query_embeddings(sentences)
if len(sentences) > max_len:
X = embeddings[:max_len]
else:
X = embeddings
X_padded = pad_sequences([X], maxlen=max_len, dtype="float32", padding="post")
return X_padded
def predict_from_document(sentences):
preprop = preprocess(sentences)
prediction = model.predict(preprop)
# Set the prediction threshold to 0.8 instead of 0.5, now use mean
output = (
prediction.flatten()[: len(sentences)]
>= np.mean(prediction) + np.std(prediction) * 2
).astype(int)
return output, prediction.flatten()[: len(sentences)]
|