Update app.py

app.py

@@ -13,42 +13,12 @@ https://github.com/pinecone-io/examples/tree/master/learn/algos-and-libraries/be
 
 # data = load_dataset('jamescalam/python-reddit')
 data = load_dataset("awacke1/LOINC-Panels-and-Forms")
-data = data.filter(
-    lambda x: True if len(x[0]) > 30 else 0
-)
-from bertopic import BERTopic
-from sklearn.feature_extraction.text import CountVectorizer
 
-# we add this to remove stopwords
-vectorizer_model = CountVectorizer(ngram_range=(1, 2), stop_words="english")
 
-model = BERTopic(
-    vectorizer_model=vectorizer_model,
-    language='english', calculate_probabilities=True,
-    verbose=True
-)
-topics, probs = model.fit_transform(text)
-freq = model.get_topic_info()
-freq.head(10)
 
 
-from sentence_transformers import SentenceTransformer
-
-model = SentenceTransformer('all-MiniLM-L6-v2')
-model
-
-import numpy as np
-from tqdm.auto import tqdm
-
-batch_size = 16
-
-embeds = np.zeros((n, model.get_sentence_embedding_dimension()))
-
-for i in tqdm(range(0, n, batch_size)):
-    i_end = min(i+batch_size, n)
-    batch = data[0][i:i_end]
-    batch_embed = model.encode(batch)
-    embeds[i:i_end,:] = batch_embed
 
+from datasets import load_dataset
 
-    
+geo = load_dataset('jamescalam/world-cities-geo', split='train')
+st.write(geo)