Time the speed

app.py

@@ -7,6 +7,7 @@ import streamlit as st
 
 import torch
 import datasets
+from tqdm import tqdm
 from transformers import AutoModelForSequenceClassification, AutoTokenizer
 
 model_name = st.text_input("Enter a model's name on HF")
@@ -58,11 +59,9 @@ def predict_top_p(text, P=0.9):
 dataset_name = "AMR-KELEG/test-dataset"
 dataset = datasets.load_dataset(dataset_name, token=os.environ["HF_TOKEN"])["test"]
 
-for sample in dataset:
+for sample in tqdm(dataset):
     text = sample["sentence"]
     labels= [DIALECTS[i] for i in range(len(DIALECTS)) if DIALECTS[i] in sample.keys() and int(sample[DIALECTS[i]]) == 1]
     pred = predict_top_p(text)
     sample["pred"] = pred
     st.write("Text:", text)
-    st.write("Labels:", labels)
-    st.write("Predictions:", pred)