Enhancing Customer Support with AI Assistance
- Leverage Meta AI's natural language processing to analyze customer support tickets, categorize them, and provide initial response suggestions for support agents to improve efficiency.
- Utilize Heroku to deploy a scalable web application that integrates seamlessly with customer support systems, automatically escalating more complex issues to human agents.
```python
from meta_nlp_api import MetaNaturalLanguageProcessing
import requests
api_key = 'your_meta_nlp_api_key'
def analyze_ticket(ticket_text):
response = requests.post("https://meta-nlp-api.com/analyze", headers={"API-KEY": api_key}, json={"input": ticket_text})
return response.json().get('suggest_category', "Uncategorized")
Heroku Flask app setup
from flask import Flask, request, jsonify
app = Flask(name)
@app.route('/support_ticket', methods=['POST'])
def support_ticket():
data = request.json
ticket_text = data.get('ticket_text', '')
category = analyze_ticket(ticket_text)
return jsonify({'category': category})
if name == 'main':
app.run()
```
Dynamic Content Recommendation Engine
- Employ Meta AI's recommendation capabilities to analyze user behavior and content preferences, delivering personalized content suggestions that increase engagement.
- Deploy a Heroku app to continually update recommendations based on user interactions and feedback, optimizing the algorithm for improved accuracy over time.
```shell
Shell script to manage Heroku environment variables for recommendation engine
heroku config:set META_API_KEY='your_meta_api_key'
Push new configurations to Heroku
git add .
git commit -m "Update configuration for content recommendation engine"
git push heroku master
```
