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import gradio as gr
import torch
from transformers import BertTokenizer, BertForSequenceClassification
MODEL_PATH = "AntiSpamInstitute/spam-detector-bert-MoE-v2.2"
tokenizer = BertTokenizer.from_pretrained(MODEL_PATH)
model = BertForSequenceClassification.from_pretrained(MODEL_PATH)
# Function to predict if a given text is Spam or Ham
def predict_spam(text):
# Tokenize the input text
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
# Perform inference
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
prediction = torch.argmax(logits, axis=-1).item()
# Map prediction to label
if prediction == 1:
return "Spam"
else:
return "Ham"
# Gradio UI - Input and Output components
inputs = gr.Textbox(label="Enter Text", placeholder="Type a message to check if it's Spam or Ham...")
outputs = gr.Label(label="Prediction")
# List of example inputs
examples = [
["Free entry in 2 a wkly comp to win FA Cup final tkts 21st May 2005. Text FA to 87121 to receive entry question(std txt rate)T&C's apply 08452810075over18's"],
["Yeah he got in at 2 and was v apologetic. n had fallen out and she was actin like spoilt child and he got caught up in that. Till 2! But we won't go there! Not doing too badly cheers. You?"]
]
gr_interface = gr.Interface(
fn=predict_spam,
inputs=inputs,
outputs=outputs,
examples=examples,
title="Spam Detection with BERT",
description="Type a message in the text box to check if it's Spam or Ham using a pre-trained BERT model."
)
gr_interface.launch() |