app.py

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()