Add app file

app.py

@@ -0,0 +1,31 @@
+import os.path
+
+import numpy as np
+import pandas as pd
+import torch
+import tqdm
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from baseline_BERT import id2label
+import gradio as gr
+
+model_ckpt = "Kithogue/2-lvl-events-multilingual"
+tokenizer = AutoTokenizer.from_pretrained(model_ckpt)
+
+
+def get_inference(sample):
+    model_hf = AutoModelForSequenceClassification.from_pretrained(model_ckpt)
+    encoding = tokenizer(sample, return_tensors="pt")
+    encoding = {k: v.to('cuda') for k, v in encoding.items()}
+    outputs = model_hf(**encoding)
+    logits = outputs.logits
+    # apply sigmoid + threshold
+    sigmoid = torch.nn.Sigmoid()
+    probs = sigmoid(logits.squeeze().cpu())
+    predictions = np.zeros(probs.shape)
+    predictions[np.where(probs >= 0.4)] = 1
+    # turn predicted id's into actual label names
+    predicted_labels = [id2label[idx] for idx, label in enumerate(predictions) if label == 1.0]
+    return predicted_labels
+
+
+gr.Interface(fn=get_inference, inputs=["text"], outputs=["text"]).launch(share=True)