Create app.py

app.py

@@ -0,0 +1,30 @@
+import gradio as gr
+import onnxruntime 
+from transformers import AutoTokenizer
+import torch, json
+
+token = AutoTokenizer.from_pretrained('distilroberta-base')
+
+types = ['toxic',
+  'severe_toxic',
+  'obscene',
+  'threat',
+  'insult',
+  'identity_hate',
+  'positive']
+
+inf_session = onnxruntime.InferenceSession('classifier-quantized.onnx')
+input_name = inf_session.get_inputs()[0].name
+output_name = inf_session.get_outputs()[0].name
+
+def classify(review):
+    input_ids = token(description)['input_ids'][:512]
+    logits = inf_session.run([output_name], {input_name: [input_ids]})[0]
+    logits = torch.FloatTensor(logits)
+    probs = torch.sigmoid(logits)[0]
+    return dict(zip(genres, map(float, probs))) 
+
+
+label = gr.outputs.Label(num_top_classes=3)
+iface = gr.Interface(fn=classify,inputs='text',outputs = label)
+iface.launch(inline=False)