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import streamlit as st
from transformers import pipeline
from ldclient import LDClient, Config, Context
import os
import torch
# Retrieve the LaunchDarkly SDK key from environment variables
ld_sdk_key = os.getenv("LAUNCHDARKLY_SDK_KEY")
# Initialize LaunchDarkly client with the correct configuration
ld_client = LDClient(Config(ld_sdk_key))
# Function to get the AI model configuration from LaunchDarkly
def get_model_config():
flag_key = "swap-sentiment-models" # Replace with your flag key
# Create context using Context builder
context = Context.builder("context-key-123abc").name("Erin").build()
flag_variation = ld_client.variation(flag_key, context, default={})
model_id = flag_variation.get("modelID", "distilbert-base-uncased")
return model_id
# Function to translate sentiment labels to user-friendly terms
def translate_label(label):
label_mapping = {
"LABEL_0": "🤬 Negative",
"LABEL_1": "😶 Neutral",
"LABEL_2": "😃 Positive"
}
return label_mapping.get(label, "Unknown")
# Streamlit app
st.title("Sentiment Analysis Demo with AI Model Flags")
user_input = st.text_area("Enter text for sentiment analysis:")
if st.button("Analyze"):
model_id = get_model_config()
model = pipeline("sentiment-analysis", model=model_id)
# Display model details
st.write(f"Using model: {model_id}")
# Perform sentiment analysis
results = model(user_input)
st.write("Results:")
# Translate and display the results
for result in results:
label = translate_label(result['label'])
score = result['score']
st.write(f"Sentiment: {label}, Confidence: {score:.2f}")
# Closing the LD client
ld_client.close()
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