app.py

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

# Load model
model_path = "yazied49/disabilityy_model_final"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForSequenceClassification.from_pretrained(model_path)

# Prediction function
def predict(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
    with torch.no_grad():
        outputs = model(**inputs)
        probs = torch.nn.functional.softmax(outputs.logits, dim=1)
        pred_id = torch.argmax(probs, dim=1).item()
        confidence = torch.max(probs).item()
        label = model.config.id2label[str(pred_id)]  # تأكد إن id2label keys = strings

    return f"{label} ({round(confidence * 100, 2)}%)"

# Create Gradio interface
demo = gr.Interface(fn=predict, inputs="text", outputs="text", title="Disability Classifier")

# Launch app
demo.launch()