Update app.py

app.py

@@ -0,0 +1,31 @@
+import gradio as gr
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
+
+# Load your model and tokenizer
+model_name = "fohake/cert"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Define the prediction function
+def predict(text):
+    inputs = tokenizer(text, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**inputs)
+    logits = outputs.logits
+    probabilities = torch.nn.functional.softmax(logits, dim=-1)
+    predicted_class = torch.argmax(probabilities, dim=-1).item()
+    confidence = probabilities[0][predicted_class].item()
+    return {"class": predicted_class, "confidence": confidence}
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.inputs.Textbox(lines=2, placeholder="Enter text here..."),
+    outputs="json",
+    title="Text Classification with CERT",
+    description="Enter a piece of text to classify it using the CERT model."
+)
+
+if __name__ == "__main__":
+    iface.launch()