app.py

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()