app.py

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

model = AutoModelForSequenceClassification.from_pretrained("MaryahGreene/arch_flava_mod", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("MaryahGreene/arch_flava_mod", trust_remote_code=True)

id2label = model.config.id2label  # make sure this is set during training!

def predict(text):
    try:
        inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
        outputs = model(**inputs)
        probs = torch.nn.functional.softmax(outputs.logits, dim=1)
        top_label = torch.argmax(probs, dim=1).item()
        label_name = id2label[str(top_label)]
        confidence = probs[0][top_label].item()
        return f"Prediction: {label_name} ({confidence:.2%} confidence)"
    except Exception as e:
        return f"❌ Error: {str(e)}"

gr.Interface(fn=predict, inputs="text", outputs="text", title="ArchFlava Predictor").launch()