api.py

from flask import Flask, request, jsonify
import streamlit as st
from transformers import pipeline
import os
from ldclient import LDClient, Config, Context

app = Flask(__name__)

# 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")


@app.route('/analyze', methods=['POST'])
def analyze_sentiment():
    data = request.json
    name = data.get('name', 'Anonymous')
    user_input = data.get('text', '')

    if not user_input:
        return jsonify({"error": "No text provided for analysis"}), 400

    model_id = get_model_config(name)
    model = pipeline("sentiment-analysis", model=model_id)

    results = model(user_input)
    translated_results = [{"Sentiment": translate_label(
        result['label']), "Confidence": result['score'], "User_input": user_input} for result in results]

    return jsonify({"name": name, "results": translated_results})


if __name__ == '__main__':
    app.run(debug=True)