Update app.py

app.py

@@ -1,4 +1,37 @@
 import streamlit as st
+from transformers import pipeline
+import launchdarkly_api
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+# Initialize LaunchDarkly client
+ld_client = launchdarkly_api.LDClient("YOUR_LAUNCHDARKLY_SDK_KEY")
+
+# Model descriptions
+model_descriptions = {
+    "bert-base-uncased": "BERT base model (uncased)",
+    "roberta-base": "RoBERTa base model",
+    "distilbert-base-uncased": "DistilBERT base model (uncased)",
+    "albert-base-v2": "ALBERT base model v2"
+}
+
+# Create a function to get the active model from LaunchDarkly
+def get_active_model():
+    if ld_client.variation("use_bert", {"key": "user"}):
+        return pipeline("sentiment-analysis", model="bert-base-uncased"), "bert-base-uncased"
+    elif ld_client.variation("use_roberta", {"key": "user"}):
+        return pipeline("sentiment-analysis", model="roberta-base"), "roberta-base"
+    elif ld_client.variation("use_distilbert", {"key": "user"}):
+        return pipeline("sentiment-analysis", model="distilbert-base-uncased"), "distilbert-base-uncased"
+    elif ld_client.variation("use_albert", {"key": "user"}):
+        return pipeline("sentiment-analysis", model="albert-base-v2"), "albert-base-v2"
+    else:
+        return pipeline("sentiment-analysis", model="distilbert-base-uncased"), "distilbert-base-uncased"  # Default model
+
+# Streamlit app
+st.title("Sentiment Analysis Demo")
+user_input = st.text_area("Enter text for sentiment analysis:")
+
+if st.button("Analyze"):
+    model, model_name = get_active_model()
+    result = model(user_input)
+    st.write(f"Model used: {model_descriptions[model_name]}")
+    st.write(result)