Create app.py

app.py

@@ -0,0 +1,35 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+MODEL_ID = "roncc13/trainCMDBERT-sample"
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+model = AutoModelForSequenceClassification.from_pretrained(MODEL_ID)
+
+# Adjust these to match your label order
+label_names = ["fake", "real"]
+
+def classify(text):
+    inputs = tokenizer(
+        text,
+        return_tensors="pt",
+        truncation=True,
+        padding=True,
+        max_length=256
+    )
+    with torch.no_grad():
+        outputs = model(**inputs)
+        probs = torch.softmax(outputs.logits, dim=-1)[0].tolist()
+    return {label_names[i]: float(probs[i]) for i in range(len(label_names))}
+
+demo = gr.Interface(
+    fn=classify,
+    inputs=gr.Textbox(lines=4, label="Enter Cebuano news text"),
+    outputs=gr.Label(num_top_classes=2, label="Prediction"),
+    title="CMD-BERT Cebuano Fake News Detector",
+    description="Paste Cebuano news text to check if it is likely fake or real."
+)
+
+if __name__ == "__main__":
+    demo.launch()