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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)
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