Delete app.py

app.py

@@ -1,30 +0,0 @@
-import streamlit as st
-from transformers import pipeline
-
-# Load the text classification model pipeline
-classifier = pipeline("text-classification",model='isom5240sp24/bert-base-uncased-emotion', return_all_scores=True)
-
-# Streamlit application title
-st.title("Text Classification for you")
-st.write("Classification for 6 emotions: sadness, joy, love, anger, fear, surprise")
-
-# Text input for user to enter the text to classify
-text = st.text_area("Enter the text to classify", "")
-
-# Perform text classification when the user clicks the "Classify" button
-if st.button("Classify"):
-    # Perform text classification on the input text
-    results = classifier(text)[0]
-    
-    # Display the classification result
-    max_score = float('-inf')
-    max_label = ''
-    
-    for result in results:
-        if result['score'] > max_score:
-            max_score = result['score']
-            max_label = result['label']
-            
-    st.write("Text:", text)
-    st.write("Label:", max_label)
-    st.write("Score:", max_score)