import streamlit as st
from transformers import pipeline
from ldclient import LDClient, Config, Context
import os

# 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(user_name):
    flag_key = "model-swap"  # Replace with your flag key

    # Create a context using Context Builder—it can be anything, but for this use case, I’m just defaulting to myself.

    context = Context.builder(f"context-key-{user_name}").name(user_name).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",
        "1 star": "🤬 Negative",
        "2 stars": "🤬 Negative",
        "3 stars": "😶 Neutral",
        "4 stars": "😃 Positive",
        "5 stars": "😃 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:")

# Add an input box for the user to enter their name
name = st.text_input("Enter your name", "AJ")

# if no name is anter add anonymous
if not name:
    name = "Anonymous"

if st.button("Analyze"):
    model_id = get_model_config(name)
    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()