File size: 2,457 Bytes
2427679 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 |
#importing the necessary libraries
import gradio as gr
import numpy as np
import pandas as pd
import re
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from topic_labels import labels
#Defining the models and tokenuzer
model_name = "valurank/distilroberta-topic-classification"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
#model.to(device)
tokenizer = AutoTokenizer.from_pretrained(model_name)
def clean_text(raw_text):
text = raw_text.encode("ascii", errors="ignore").decode(
"ascii"
) # remove non-ascii, Chinese characters
text = re.sub(r"\n", " ", text)
text = re.sub(r"\n\n", " ", text)
text = re.sub(r"\t", " ", text)
text = text.strip(" ")
text = re.sub(
" +", " ", text
).strip() # get rid of multiple spaces and replace with a single
text = re.sub(r"Date\s\d{1,2}\/\d{1,2}\/\d{4}", "", text) #remove date
text = re.sub(r"\d{1,2}:\d{2}\s[A-Z]+\s[A-Z]+", "", text) #remove time
return text
def find_two_highest_indices(arr):
if len(arr) < 2:
raise ValueError("Array must have at least two elements")
# Initialize the indices of the two highest values
max_idx = second_max_idx = None
for i, value in enumerate(arr):
if max_idx is None or value > arr[max_idx]:
second_max_idx = max_idx
max_idx = i
elif second_max_idx is None or value > arr[second_max_idx]:
second_max_idx = i
return max_idx, second_max_idx
def predict_topic(text):
text = clean_text(text)
dict_topic = {}
input_tensor = tokenizer.encode(text, return_tensors="pt", truncation=True)
logits = model(input_tensor).logits
softmax = torch.nn.Softmax(dim=1)
probs = softmax(logits)[0]
probs = probs.cpu().detach().numpy()
max_index = find_two_highest_indices(probs)
emotion_1, emotion_2 = labels[max_index[0]], labels[max_index[1]]
probs_1, probs_2 = probs[max_index[0]], probs[max_index[1]]
dict_topic[emotion_1] = round((probs_1), 2)
#if probs_2 > 0.01:
dict_topic[emotion_2] = round((probs_2), 2)
return dict_topic
#Creating the interface for the radio appdemo = gr.Interface(multi_label_emotions, inputs=gr.Textbox(),
demo = gr.Interface(predict_topic, inputs=gr.Textbox(),
outputs = gr.Label(num_top_classes=2),
title="Topic Classification")
if __name__ == "__main__":
demo.launch(debug=True)
|