Add inference script

inference.py

@@ -0,0 +1,18 @@
+from transformers import BertForSequenceClassification, BertTokenizer
+import torch
+import json
+import requests
+
+def predict(text):
+    repo_id = "logasanjeev/goemotions-bert"
+    model = BertForSequenceClassification.from_pretrained(repo_id)
+    tokenizer = BertTokenizer.from_pretrained(repo_id)
+    thresholds_url = f"https://huggingface.co/{repo_id}/raw/main/thresholds.json"
+    thresholds_data = json.loads(requests.get(thresholds_url).text)
+    emotion_labels = thresholds_data["emotion_labels"]
+    thresholds = thresholds_data["thresholds"]
+    encodings = tokenizer(text, padding='max_length', truncation=True, max_length=128, return_tensors='pt')
+    with torch.no_grad():
+        logits = torch.sigmoid(model(**encodings).logits).numpy()[0]
+    predictions = [{"label": emotion_labels[i], "score": float(logit)} for i, (logit, thresh) in enumerate(zip(logits, thresholds)) if logit >= thresh]
+    return sorted(predictions, key=lambda x: x["score"], reverse=True)