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import gradio as gr
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import numpy as np
from scipy.special import softmax
import csv
import urllib.request
# Preprocess text (username and link placeholders)
def preprocess(text):
new_text = []
for t in text.split(" "):
t = '@user' if t.startswith('@') and len(t) > 1 else t
t = 'http' if t.startswith('http') else t
new_text.append(t)
return " ".join(new_text)
def classify_text(text):
# Tasks: emoji, emotion, hate, irony, offensive, sentiment
# stance/abortion, stance/atheism, stance/climate, stance/feminist, stance/hillary
task = 'emoji'
MODEL = f"cardiffnlp/twitter-roberta-base-{task}"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
# Download label mapping
labels = []
mapping_link = f"https://raw.githubusercontent.com/cardiffnlp/tweeteval/main/datasets/{task}/mapping.txt"
with urllib.request.urlopen(mapping_link) as f:
html = f.read().decode('utf-8').split("\n")
csvreader = csv.reader(html, delimiter='\t')
labels = [row[1] for row in csvreader if len(row) > 1]
# Load model
model = AutoModelForSequenceClassification.from_pretrained(MODEL)
text = preprocess(text)
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
scores = output.logits[0].detach().numpy()
scores = softmax(scores)
ranking = np.argsort(scores)
ranking = ranking[::-1]
results = []
for i in range(scores.shape[0]):
label = labels[ranking[i]]
score = scores[ranking[i]]
result = f"{i+1}) {label} {np.round(float(score), 4)}"
results.append(result)
return results
iface = gr.Interface(
fn=classify_text,
inputs="text",
outputs="text",
title="Text Classification",
description="Classify the text into different categories.",
example="Looking forward to Christmas"
)
iface.launch()
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