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6e7acaa | 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 | import re
import torch
import torch.nn.functional as F
import gradio as gr
from transformers import AutoTokenizer, AutoModelForSequenceClassification
MODEL_DIR = "bert_classifier" # folder you uploaded to the Space
MAX_LENGTH = 128
LABELS = {0: "🎭 Meme", 1: "📰 Real Event"}
_URL_RE = re.compile(r"https?://\S+|www\.\S+")
_MENTION_RE = re.compile(r"@\w+")
_HASHTAG_RE = re.compile(r"#")
_NON_WORD_RE = re.compile(r"[^a-z0-9\s]")
_WS_RE = re.compile(r"\s+")
def clean_tweet(text: str) -> str:
t = text.lower()
t = _URL_RE.sub(" ", t)
t = _MENTION_RE.sub(" ", t)
t = _HASHTAG_RE.sub(" ", t)
t = _NON_WORD_RE.sub(" ", t)
return _WS_RE.sub(" ", t).strip()
device = "cuda" if torch.cuda.is_available() else "cpu"
tokenizer = AutoTokenizer.from_pretrained(MODEL_DIR)
model = AutoModelForSequenceClassification.from_pretrained(MODEL_DIR)
model.to(device).eval()
@torch.no_grad()
def classify(text: str):
if not text.strip():
return "Please enter a tweet."
cleaned = clean_tweet(text)
enc = tokenizer(cleaned, truncation=True, max_length=MAX_LENGTH, return_tensors="pt").to(device)
probs = F.softmax(model(**enc).logits[0], dim=-1).cpu().numpy()
pred = int(probs.argmax())
return {
"Label": LABELS[pred],
"Confidence": f"{probs[pred]:.1%}",
"P(meme)": f"{probs[0]:.1%}",
"P(real)": f"{probs[1]:.1%}",
}
gr.Interface(
fn=classify,
inputs=gr.Textbox(lines=3, placeholder="Paste a tweet here..."),
outputs=gr.JSON(),
title="Meme vs Real Event Classifier",
examples=[
["Massive 6.5 earthquake just hit Istanbul, buildings swaying"],
["skibidi toilet ohio rizz level 9000 fr fr 💀"],
["AWS us-east-1 throwing 500s across the board"],
]
).launch() |