Create app.py

app.py

@@ -0,0 +1,36 @@
+import streamlit as st
+from transformers import pipeline
+import json
+
+# Load the text classification model from Hugging Face
+@st.cache_resource
+def load_model():
+    model_name = "annyalvarez/MoralS-BERT"
+    return pipeline("text-classification", model=model_name)
+
+model = load_model()
+
+# Streamlit app interface
+st.title("Moral Values Text Classification")
+
+# Text input from the user
+user_input = st.text_area("Enter text for classification:", "")
+
+if st.button("Classify"):
+    if user_input:
+        # Get model predictions
+        predictions = model(user_input)
+
+        # Sort predictions by score
+        predictions = sorted(predictions, key=lambda x: x['score'], reverse=True)
+
+        # Format the predictions as required
+        output = [{
+            "label": pred['label'],
+            "score": pred['score']
+        } for pred in predictions]
+
+        # Display the output
+        st.json(output)
+    else:
+        st.write("Please enter some text to classify.")