| 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() | |