app.py

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

# Load model & tokenizer from the Space
model = AutoModelForSequenceClassification.from_pretrained(".")
tokenizer = AutoTokenizer.from_pretrained(".")

category_mapping = {
    0: "Q/E", 1: "DA", 2: "V", 3: "DM", 4: "P",
    5: "DS", 6: "EAT", 7: "AM", 8: "Other", 9: "TSC"
}

def predict(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding="max_length", max_length=512)
    
    with torch.no_grad():
        logits = model(inputs["input_ids"], inputs["attention_mask"])
        probs = torch.softmax(logits, dim=1).numpy()
        pred_class = int(np.argmax(probs))

    category_name = category_mapping.get(pred_class, "Unknown")
    return f"Predicted Category: {category_name} (Code: {pred_class})"

iface = gr.Interface(fn=predict, inputs="text", outputs="text")
iface.launch()