Upload app.py

app.py

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+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()