app.py

import torch
import gradio as gr
import os
from detoxify import Detoxify
import pandas as pd
import json
import spaces
import logging
import datetime

@spaces.GPU
def classify(query):
    model = Detoxify("unbiased-small", device="cuda")

    all_result = []
    request_type = type(query)
    try:
        data = json.loads(query)
        if type(data) != list:
            data = [query]
        else:
            request_type = type(data)
    except Exception as e:
        print(e)
        data = [query]
        pass

    for i in range(len(data)):
        result = {}
        start_time = datetime.datetime.now()
        
        df = pd.DataFrame(model.predict(str(data[i])), index=[0])
        columns = df.columns

        for i, label in enumerate(columns):
            result[label] = df[label][0].round(3).astype("float")

        end_time = datetime.datetime.now()
        elapsed_time = end_time - start_time
        result["time"] =  str(elapsed_time)

        logging.debug("elapsed predict time: %s", str(elapsed_time))
        print("elapsed predict time:", str(elapsed_time))
        
        all_result.append(result)

    
    return json.dumps(all_result) if request_type == list else all_result[0]

demo = gr.Interface(fn=classify, inputs=["text"], outputs="text")
demo.launch()