Update app.py

app.py

@@ -13,8 +13,11 @@ dataseteCQM = load_dataset("awacke1/eCQM-Code-Value-Semantic-Set.csv")
 from transformers import AutoTokenizer
 tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
 dataset = datasetLOINC.map(lambda examples: tokenizer(examples["Description"]), batched=True)
-JSONOBJ=dataset['train'][0]
+JSONOBJ2=dataset['train'][0]
 
+sw = datasetLOINC.filter(lambda example: example["Description"].startswith("Allergy"))
+len(sw)
+print(sw)
 print(datasetLOINC)
 print(datasetSNOMED)
 print(dataseteCQM)
@@ -34,7 +37,7 @@ import gradio as gr
 
 HF_TOKEN = os.environ.get("HF_TOKEN")
 CHOICES = ["SNOMED", "LOINC", "CQM"]
-#JSONOBJ = """{"items":{"item":[{"id": "0001","type": null,"is_good": false,"ppu": 0.55,"batters":{"batter":[{ "id": "1001", "type": "Regular" },{ "id": "1002", "type": "Chocolate" },{ "id": "1003", "type": "Blueberry" },{ "id": "1004", "type": "Devil's Food" }]},"topping":[{ "id": "5001", "type": "None" },{ "id": "5002", "type": "Glazed" },{ "id": "5005", "type": "Sugar" },{ "id": "5007", "type": "Powdered Sugar" },{ "id": "5006", "type": "Chocolate with Sprinkles" },{ "id": "5003", "type": "Chocolate" },{ "id": "5004", "type": "Maple" }]}]}}"""
+JSONOBJ = """{"items":{"item":[{"id": "0001","type": null,"is_good": false,"ppu": 0.55,"batters":{"batter":[{ "id": "1001", "type": "Regular" },{ "id": "1002", "type": "Chocolate" },{ "id": "1003", "type": "Blueberry" },{ "id": "1004", "type": "Devil's Food" }]},"topping":[{ "id": "5001", "type": "None" },{ "id": "5002", "type": "Glazed" },{ "id": "5005", "type": "Sugar" },{ "id": "5007", "type": "Powdered Sugar" },{ "id": "5006", "type": "Chocolate with Sprinkles" },{ "id": "5003", "type": "Chocolate" },{ "id": "5004", "type": "Maple" }]}]}}"""
 
 
 def profile_dataset(dataset=datasetSNOMED, username="awacke1", token=HF_TOKEN, dataset_name="awacke1/SNOMED-CT-Code-Value-Semantic-Set.csv"):