app.py

import gradio as gr
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch

# Load your model and tokenizer
model_name = "fohake/cert"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Define the prediction function
def predict(text):
    inputs = tokenizer(text, return_tensors="pt")
    with torch.no_grad():
        outputs = model(**inputs)
    logits = outputs.logits
    probabilities = torch.nn.functional.softmax(logits, dim=-1)
    predicted_class = torch.argmax(probabilities, dim=-1).item()
    confidence = probabilities[0][predicted_class].item()
    return {"class": predicted_class, "confidence": confidence}

# Create the Gradio interface
iface = gr.Interface(
    fn=predict,
    inputs=gr.inputs.Textbox(lines=2, placeholder="Enter text here..."),
    outputs="json",
    title="Text Classification with CERT",
    description="Enter a piece of text to classify it using the CERT model."
)

if __name__ == "__main__":
    iface.launch()