version alpha

app.py

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+import gradio as gr
+import onnxruntime
+import numpy as np
+import pickle
+threshold = 0.5
+onnx_session = onnxruntime.InferenceSession("bert.onnx")
+
+# Load the instance back
+with open('classes.pkl', 'rb') as file:
+    mlb = pickle.load(file)
+
+with open('tokenizer.pkl', 'rb') as file:
+    tokenizer = pickle.load(file)
+
+
+# Create a function to predict tags using the ONNX model
+def predict_tags_onnx(text):
+    encoded_text = tokenizer(text , padding=True, truncation=True, return_tensors='pt')
+    input_ids = encoded_text["input_ids"].numpy()
+    attention_mask = encoded_text["attention_mask"].numpy()
+
+    # Run the ONNX model
+    outputs = np.asarray(onnx_session.run(None, {"input_ids": input_ids , "attention_mask":attention_mask}))
+
+    # Post-process the outputs as needed
+    #predicted_labels = torch.sigmoid(outputs).cpu().numpy()
+    predicted_tags = mlb.classes_[np.where(np.squeeze((outputs > threshold).astype(int)).flatten() == 1)]
+
+    return predicted_tags
+
+iface = gr.Interface(fn=predict_tags_onnx, inputs="text", outputs="text")
+iface.launch()

classes.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:203390ce8d1e08f4ce6f73a1216738464c89d23caec7ec32e122936a26a90412
+size 922

requirements.txt

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+onnxruntime
+pytorch

tokenizer.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:add9a91ca5f8f6831904391f6f9b37477fbd1b36ece1032c100b42810a930951
+size 628896