and now the counter

language_models_project/app.py

@@ -1,14 +1,20 @@
 import streamlit as st  #Web App
 from main import classify
 
+demo_phrases = """ Here are some examples:
+this is a phrase
+is it neutral
+nothing else to say
+man I'm so damn angry
+sarcasm lol
+I love this product
+"""
 
 #title
-st.title("Easy OCR - Extract Text from Images")
-
-
+st.title("Sentiment Analysis")
 
 #subtitle
-st.markdown("## Optical Character Recognition - Using `easyocr`, `streamlit` -  hosted on 🤗 Spaces")
+st.markdown("## A selection of popular sentiment analysis models -  hosted on 🤗 Spaces")
 
 model_name = st.selectbox(
     'Select a pre-trained model',
@@ -19,17 +25,17 @@ model_name = st.selectbox(
     ],
 )
 
-input_sentences = st.text_area("Sentences", value="", height=200)
+input_sentences = st.text_area("Sentences", value=demo_phrases, height=200)
 
 data = input_sentences.split('\n')
 
 if st.button("Classify"):
-    for i in data:
-        st.write(i)
-        j = classify(model_name.strip(), i)[0]
+    st.write("Please allow a few minutes for the model to run/download")
+    for i in range(len(data)):
+        j = classify(model_name.strip(), data[i])[0]
         sentiment = j['label']
         confidence = j['score']
-        st.write(f"{i} :: Classification - {sentiment} with confidence {confidence}")
+        st.write(f"{i}. {data[i]} :: Classification - {sentiment} with confidence {confidence}")
  
 
 st.markdown("Link to the app - [image-to-text-app on 🤗 Spaces](https://huggingface.co/spaces/Amrrs/image-to-text-app)")