Update app.py

app.py

@@ -1,28 +1,30 @@
 import streamlit as st
-from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer
+from transformers import pipeline
 
-# Load the emotion text classification model
-model_name = "danielferreira/emotion-text-classification"
-model = AutoModelForSequenceClassification.from_pretrained(model_name)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-emotion_classifier = pipeline('text-classification', model=model, tokenizer=tokenizer)
+# Load the sentiment analysis model from Hugging Face
+sentiment_analysis = pipeline("sentiment-analysis")
+
+# Function to get sentiment prediction
+def predict_sentiment(text):
+    result = sentiment_analysis(text)
+    label = result[0]['label']
+    score = result[0]['score']
+    return label, score
 
 # Streamlit web app
 def main():
-    st.title("Emotion Text Classification App")
+    st.title("Sentiment Analysis App")
 
     # Input text area
     user_input = st.text_area("Enter text:", "Type or paste your text here...")
 
-    # Button to trigger classification
-    if st.button("Classify Text Emotion"):
-        # Classification result
-        classification_result = emotion_classifier(user_input)
-        
+    # Button to trigger sentiment prediction
+    if st.button("Predict Sentiment"):
         # Display the result
-        st.write("Predicted Emotion:", classification_result[0]['label'])
-        st.write("Score:", classification_result[0]['score'])
+        sentiment_label, sentiment_score = predict_sentiment(user_input)
+        st.write(f"Sentiment: {sentiment_label}, Score: {sentiment_score:.4f}")
 
 # Run the app
 if __name__ == "__main__":
-    main()
+    main()
+