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File size: 3,055 Bytes
3ed9870 3dfac77 ccc2666 3ed9870 4839ba0 3ed9870 a426081 3ed9870 09aaef7 461d454 3ed9870 461d454 e8e7821 3ed9870 | 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 | import tensorflow as tf
from transformers import BertTokenizer, TFBertForSequenceClassification
import numpy as np
import json
import requests
import gradio as gr
import logging
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 predict_text(params):
try:
params = json.loads(params)
except JSONDecodeError as e:
logging.error(f"Invalid JSON input: {e.msg} at line {e.lineno} column {e.colno}")
return {"error": f"Invalid JSON input: {e.msg} at line {e.lineno} column {e.colno}"}
texts = params.get("urls",[])
if not params.get("normalfileID",[]):
file_ids = [None]*len(texts)
else:
file_ids = params.get("normalfileID",[])
# api = params.get("api", "")
# job_id = params.get("job_id","")
if not texts:
return {"error": "Missing required parameters: 'texts'"}
solutions = []
for text,file_id in zip(texts,file_ids):
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]
print(f"{logits}\t{pred_label}")
if not pred_label:
predict_label = 7
label = {0: 'BUSINESS', 1: 'COMEDY', 2: 'CRIME', 3: 'FOOD & DRINK', 4: 'POLITICS', 5: 'SPORTS', 6: 'TRAVEL', 7: 'None'}
result = {'text':text, 'answer':[label[pred_label]], "qcUser" : None,"normalfileID":file_id}
solutions.append(result)
# result_url = f"{api}/{job_id}"
# send_results_to_api(solutions, result_url)
return json.dumps({"solutions":solutions})
inputt = gr.Textbox(label="Parameters in Json Format... Eg. {'texts':['text1', 'text2']")
outputt = gr.JSON()
application = gr.Interface(fn = predict_text, inputs = inputt, outputs = outputt, title='Multi Text Classification with API Integration..')
application.launch() |