Spaces:
Build error
Build error
File size: 2,297 Bytes
e722aa1 a989607 922b2f5 e722aa1 | 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 | import tensorflow as tf
from transformers import BertTokenizer, TFBertForSequenceClassification
import numpy as np
import json
import requests
import gradio as gr
bert_tokenizer = BertTokenizer.from_pretrained('MultiTokenizer_ep10')
bert_model = TFBertForSequenceClassification.from_pretrained('MultiModel_ep10')
def send_results_to_api(data, result_url):
headers = {'Content-Type':'application/json'}
response = requests.post(result_url, json = data, headers=headers)
if response.status_code == 200:
return response.json
else:
return {'error':f"failed to send result to API: {response.status_code}"}
def process_audio(params):
params = json.loads(params)
texts = params.get("texts",[])
api = params.get("api", "")
job_id = params.get("job_id","")
solutions = []
for text in texts:
encoding = bert_tokenizer.encode_plus(
text,
add_special_tokens=True,
max_length=128,
return_token_type_ids=True,
padding = 'max_length',
truncation=True,
return_attention_mask=True,
return_tensors='tf'
)
input_ids = encoding['input_ids']
token_type_ids = encoding['token_type_ids']
attention_mask = encoding['attention_mask']
pred = bert_model.predict([input_ids, token_type_ids, attention_mask])
logits = pred.logits
pred_label = tf.argmax(logits, axis=1).numpy()[0]
label = {1:'positive', 0:'negative'}
result = {'text':text, 'label':label[pred_label]}
solutions.append(result)
result_url = f"{api}/{job_id}"
send_results_to_api(solutions, result_url)
return json.dumps({"solutions":solutions}, indent=4)
inputt = gr.Textbox(label="Parameters in Json Format...")
outputt = gr.JSON()
application = gr.Interface(fn = process_audio(), inputs = inputt, outputs = outputt, title='Multi Text Classification with API Integration..')
application.launch() |