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import gradio as gr | |
from transformers import AutoModelForSequenceClassification, AutoTokenizer | |
model = AutoModelForSequenceClassification.from_pretrained("inkleaves/spam_detection_model") | |
tokenizer = AutoTokenizer.from_pretrained("inkleaves/spam_detection_model") | |
def predict_spam(text): | |
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True) | |
outputs = model(**inputs) | |
prediction = outputs.logits.argmax(dim=-1).item() | |
return "Spam" if prediction == 1 else "Not Spam" | |
# interface = gr.Interface(fn=predict, inputs="text", outputs="text") | |
#interface.launch(share=True) | |
# Create the Gradio interface | |
app = gr.Interface( | |
fn=predict_spam, | |
inputs="text", | |
outputs="text", | |
live=False, | |
title="Spam Detection", # Title of the app | |
description="This app classifies text as either Spam or Ham.", # Description of the app | |
) | |
# Add a custom header in larger, bolded text using HTML | |
header = gr.HTML("<h1 style='font-size:36px; font-weight:bold;'>Spam Detection App</h1>") | |
# Launch the app with the header displayed above the interface | |
#header.launch(share=True) # Launching header | |
app.launch(share=True) # Launching app |