Create app.py

app.py

@@ -0,0 +1,24 @@
+import gradio as gr
+from optimum.onnxruntime import ORTModelForSequenceClassification
+from transformers import pipeline, AutoTokenizer
+
+# Load tokenizer
+tokenizer = AutoTokenizer.from_pretrained("iam-tsr/finetuned-distilbert-employ-feedback")
+
+# Load Quantized ONNX model
+onnx_filename = "model_qint8.onnx"
+model = ORTModelForSequenceClassification.from_pretrained("iam-tsr/finetuned-distilbert-employ-feedback", file_name=onnx_filename)
+
+def pred(model, tokenizer, text):
+    
+    pipe = pipeline(task="text-classification", model=model, tokenizer=tokenizer, device="cpu")
+    return pipe(text)[0]['label']
+
+demo = gr.Interface(
+    fn=lambda text: pred(model, tokenizer, text),
+    inputs=["text"],
+    outputs=["text"],
+    api_name="predict"
+)
+
+demo.launch(share=True)