Spaces:
Runtime error
Runtime error
File size: 4,460 Bytes
939c80c 3affa92 6bc2af2 939c80c 3affa92 ef6e6ca 3affa92 aea79b5 3affa92 939c80c 3affa92 fcf5457 3affa92 402700e 939c80c 3affa92 939c80c 3e07850 24a888d 3e07850 939c80c |
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 |
from flask import Flask, request, jsonify
import asyncio
from hypercorn.asyncio import serve
from hypercorn.config import Config
import torch.nn.functional as F
from torch import nn
import os
os.environ['CURL_CA_BUNDLE'] = ''
app = Flask(__name__)
from sentence_transformers import SentenceTransformer
sentencemodel = SentenceTransformer('johnpaulbin/toxic-gte-small-3')
USE_GPU = False
""" Use torchMoji to predict emojis from a single text input
"""
import numpy as np
import emoji, json
from torchmoji.global_variables import PRETRAINED_PATH, VOCAB_PATH
from torchmoji.sentence_tokenizer import SentenceTokenizer
from torchmoji.model_def import torchmoji_emojis
import torch
# Emoji map in emoji_overview.png
EMOJIS = ":joy: :unamused: :weary: :sob: :heart_eyes: \
:pensive: :ok_hand: :blush: :heart: :smirk: \
:grin: :notes: :flushed: :100: :sleeping: \
:relieved: :relaxed: :raised_hands: :two_hearts: :expressionless: \
:sweat_smile: :pray: :confused: :kissing_heart: :heartbeat: \
:neutral_face: :information_desk_person: :disappointed: :see_no_evil: :tired_face: \
:v: :sunglasses: :rage: :thumbsup: :cry: \
:sleepy: :yum: :triumph: :hand: :mask: \
:clap: :eyes: :gun: :persevere: :smiling_imp: \
:sweat: :broken_heart: :yellow_heart: :musical_note: :speak_no_evil: \
:wink: :skull: :confounded: :smile: :stuck_out_tongue_winking_eye: \
:angry: :no_good: :muscle: :facepunch: :purple_heart: \
:sparkling_heart: :blue_heart: :grimacing: :sparkles:".split(' ')
def top_elements(array, k):
ind = np.argpartition(array, -k)[-k:]
return ind[np.argsort(array[ind])][::-1]
with open("vocabulary.json", 'r') as f:
vocabulary = json.load(f)
st = SentenceTokenizer(vocabulary, 100)
emojimodel = torchmoji_emojis("pytorch_model.bin")
if USE_GPU:
emojimodel.to("cuda:0")
def deepmojify(sentence, top_n=5, prob_only=False):
list_emojis = []
def top_elements(array, k):
ind = np.argpartition(array, -k)[-k:]
return ind[np.argsort(array[ind])][::-1]
tokenized, _, _ = st.tokenize_sentences([sentence])
tokenized = np.array(tokenized).astype(int) # convert to float first
if USE_GPU:
tokenized = torch.tensor(tokenized).cuda() # then convert to PyTorch tensor
prob = emojimodel.forward(tokenized)[0]
if not USE_GPU:
prob = torch.tensor(prob)
if prob_only:
return prob
emoji_ids = top_elements(prob.cpu().numpy(), top_n)
emojis = map(lambda x: EMOJIS[x], emoji_ids)
list_emojis.append(emoji.emojize(f"{' '.join(emojis)}", language='alias'))
# returning the emojis as a list named as list_emojis
return list_emojis, prob
model = nn.Sequential(
nn.Linear(448, 300), # Increase the number of neurons
nn.ReLU(),
nn.BatchNorm1d(300), # Batch normalization
nn.Linear(300, 300), # Increase the number of neurons
nn.ReLU(),
nn.BatchNorm1d(300), # Batch normalization
nn.Linear(300, 200), # Increase the number of neurons
nn.ReLU(),
nn.BatchNorm1d(200), # Batch normalization
nn.Linear(200, 125), # Increase the number of neurons
nn.ReLU(),
nn.BatchNorm1d(125), # Batch normalization
nn.Linear(125, 2),
nn.Dropout(0.05) # Dropout
)
model.load_state_dict(torch.load("large.pth", map_location=torch.device('cpu')))
model.eval()
@app.route('/infer', methods=['POST'])
def translate():
data = request.get_json()
TEXT = data['text'].lower()
probs = deepmojify(TEXT, prob_only=True)
embedding = sentencemodel.encode(TEXT, convert_to_tensor=True)
INPUT = torch.cat((probs, embedding))
output = F.softmax(model(INPUT.view(1, -1)), dim=1)
if output[0][1] > 0.68:
output = "true"
else:
output = "false"
return output
@app.route('/inferverbose', methods=['POST'])
def translateverbose():
data = request.get_json()
TEXT = data['text'].lower()
probs = deepmojify(TEXT, prob_only=True)
embedding = sentencemodel.encode(TEXT, convert_to_tensor=True)
INPUT = torch.cat((probs, embedding))
output = F.softmax(model(INPUT.view(1, -1)), dim=1)
if output[0][1] > 0.4:
output = "true" + str(output[0][1])
else:
output = "false" + str(output[0][0])
return output
# Define more routes for other operations like download_model, etc.
if __name__ == "__main__":
config = Config()
config.bind = ["0.0.0.0:7860"] # You can specify the host and port here
asyncio.run(serve(app, config)) |