Spaces:
Build error
Build error
| 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("texts",[]) | |
| # api = params.get("api", "") | |
| # job_id = params.get("job_id","") | |
| if not texts: | |
| return {"error": "Missing required parameters: 'urls'"} | |
| 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 = {0: 'BUSINESS', 1: 'COMEDY', 2: 'CRIME', 3: 'FOOD & DRINK', 4: 'POLITICS', 5: 'SPORTS', 6: 'TRAVEL'} | |
| 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}) | |
| 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() |