Create app.py

app.py

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+import streamlit as st
+from transformers import pipeline
+
+# Load the sentiment analysis model pipeline
+classifier = pipeline("text-classification",model='Ryleeeee/CustomSentimentModel', return_all_scores=True)
+
+# Streamlit application title and background image
+st.image("./header.png")
+st.title("Sentiment Analysis")
+st.write("Setiment classification: positive, netural, negative")
+
+# User can enter the customer review
+text = st.text_area("Enter the customer review", "")
+
+# Perform sentiment analysis when the user clicks the "Classify sentiment" button
+if st.button("Classify sentiment"):
+    # Perform sentiment analysis 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("Label:", max_label)
+    st.write("Score:", max_score)
+