create app.py

app.py

@@ -0,0 +1,27 @@
+import torch
+import gradio as gr
+from transformers import AutoConfig
+from your_model_code import CustomClassifier
+
+MODEL_ID = "your-username/custom-classifier-demo"
+config = AutoConfig.from_pretrained(MODEL_ID)
+model = CustomClassifier.from_pretrained(MODEL_ID)
+model.eval()
+
+def predict(input_csv: str):
+    vec = [float(x) for x in input_csv.split(",")]
+    if len(vec) != config.input_dim:
+        return f"Error: Need {config.input_dim} floats"
+    x = torch.tensor([vec])
+    with torch.no_grad():
+        logits = model(input_ids=x)["logits"]
+    pred = logits.argmax(dim=-1).item()
+    return f"Predicted class: {pred}"
+
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Textbox(label="Input Vector (comma-separated)"),
+    outputs="text",
+    title="Custom Classifier Demo",
+)
+demo.launch()