Create app.py

app.py

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+import gradio as gr
+import onnxruntime
+import transformers import AutoTokenizer
+import torch
+
+
+token  = AutoTokenizer.from_pretrained('distilroberta-base')
+
+inf_session = onnxruntime.InferenceSession('classifier1-quantized.onnx')
+input_name = inf_session.get_inputs()[0].name
+output_name = inf_session.get_outputs()[0].name
+
+classes = ['Art', 'Astrology', 'Biology', 'Chemistry', 'Economics', 'History', 'Literature', 'Philosophy', 'Physics', 'Politics', 'Psychology', 'Sociology']
+
+def classify(review):
+    input_ids = token(review)['input_ids'][:512]
+    logits = inf_session.run([output_name],{input_name : [input_ids]})[0]
+    logits = torch.FloatTensor(logits)
+    prob = torch.sigmoid(logits)[0]
+    return dict(zip(classes,map(float,probs)))
+
+label = gr.outputs.Label(num_top_classes=5)
+iface = gr.Interface(fn=classify,inputs='text',outputs = label)
+iface.launch(inline=False)
+
+
+
+