rifatramadhani's picture
refactor: output structure
b18d110
import torch
import gradio as gr
import os
from detoxify import Detoxify
import pandas as pd
import json
import spaces
import logging
import datetime
# Load model for first time cache
model = Detoxify("unbiased-small")
@spaces.GPU
def classify(query):
torch_device = "cuda" if torch.cuda.is_available() else "cpu"
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
start_time = datetime.datetime.now()
for i in range(len(data)):
result = {}
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")
all_result.append(result)
end_time = datetime.datetime.now()
elapsed_time = end_time - start_time
logging.debug("elapsed predict time: %s", str(elapsed_time))
print("elapsed predict time:", str(elapsed_time))
output = {}
output["time"] = str(elapsed_time)
output["device"] = torch_device
output["result"] = all_result
return json.dumps(output)
demo = gr.Interface(fn=classify, inputs=["text"], outputs="text")
demo.launch()