Update app.py

app.py

@@ -7,6 +7,7 @@ from transformers import AutoTokenizer, AutoModelForSequenceClassification
 from nltk.tokenize import sent_tokenize
 import plotly.express as px
 import time
+import tqdm
 nltk.download('punkt')
 
 # Define the model and tokenizer
@@ -140,7 +141,7 @@ iface2 = gr.Interface(fn=predict_sdg,
 
 # UPLOAD CSV
 # Define the prediction function
-def predict_sdg_from_csv(file):
+def predict_sdg_from_csv(file, progress=gr.Progress()):
     # Read the CSV file
     df_docs = pd.read_csv(file)
     text_list = df_docs["text_inputs"].tolist()
@@ -170,7 +171,7 @@ def predict_sdg_from_csv(file):
     prediction_score = []
 
     # Preprocess text and make predictions
-    for text_input in text_list:
+    for text_input in progress.tqdm(text_list, desc="Analysing data":
         time.sleep(0.02)  # Sleep to avoid rate limiting
         cleaned_text = prep_text(text_input)
         tokenized_text = tokenizer(cleaned_text, return_tensors="pt", truncation=True, max_length=512, padding=True)
@@ -222,4 +223,4 @@ demo = gr.TabbedInterface(interface_list = [iface1, iface2, iface3],
                          )
 
 # Run the interface
-demo.launch()
+demo.queue().launch()