app.py

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

# Load the model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("huggingface_user/my-model-name")  # Use your model name here
model = AutoModelForSequenceClassification.from_pretrained("huggingface_user/my-model-name")

# Function to classify input text
def classify_text(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
    with torch.no_grad():
        logits = model(**inputs).logits
    predicted_class = logits.argmax().item()  # Get the predicted class
    return f"Predicted class: {predicted_class}"

# Gradio Interface
demo = gr.Interface(fn=classify_text, inputs="text", outputs="text")
demo.launch()