new

app.py

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+import gradio as gr
+import onnxruntime as rt
+from transformers import AutoTokenizer
+import torch, json
+
+tokenizer = AutoTokenizer.from_pretrained("distilroberta-base")
+
+with open("types_encoded.json", "r") as fp:
+  encode_types = json.load(fp)
+
+recipe = list(encode_types.keys())
+
+inf_session = rt.InferenceSession('recipe-classifier-quantized.onnx')
+input_name = inf_session.get_inputs()[0].name
+output_name = inf_session.get_outputs()[0].name
+
+def classify_recipe(description):
+  input_ids = tokenizer(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(recipe, map(float, probs))) 
+
+label = gr.outputs.Label(num_top_classes=5)
+
+
+if __name__ == "__main__":
+    iface = gr.Interface(fn=classify_recipe, inputs="text", outputs=label)
+    iface.launch(inline=False)

requirements.txt

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+gradio==3.17.0
+onnxruntime==1.13.1
+torch==1.13.1
+transformers==4.26.0