initial commit

given a string of the format "ssid" + "OUI", classifies the input among the following list: ['Bus', 'Portable Device', 'Stationary Device', 'Vehicle'].

app.py

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+import tensorflow as tf
+from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
+import gradio as gr
+import numpy as np
+from scipy.special import softmax
+
+tokenizer = AutoTokenizer.from_pretrained('roberta-base')
+
+from transformers import TFAutoModelForSequenceClassification
+model = TFAutoModelForSequenceClassification.from_pretrained("ilan541/ssid_classification")
+
+
+def get_probs(text):
+  inp = tokenizer(text,
+                  truncation=True,
+                  padding='max_length',
+                  max_length=30,
+                  return_tensors='tf')
+  y_pred = model(inp)
+
+  return y_pred.logits
+
+
+def predict(your_text):
+  
+  y_pred_logits = get_probs(your_text)
+  labels = ['Bus', 'Portable Device', 'Stationary Device', 'Vehicle']
+  
+  # print logits
+  for label, logit in zip(labels, y_pred_logits):
+    print(f"logit - {label}: {logit}")
+  
+  # print probas
+  y_probs = softmax(y_pred_logits)
+  for label, prob in zip(labels, y_probs):
+    print(f"prob - {label}: {prob}")
+  
+  # print predicted class
+  i = np.argmax(y_pred_logits)
+  print(f"Predicted class: {labels[i]} with proba {y_probs[i]}')
+  
+
+iface = gr.Interface(fn=predict, inputs="text", outputs="text")
+iface.launch()