AI Customer Support Ticket Routing Project

"""Route support tickets to a queue with a confidence threshold."""

from __future__ import annotations

from dataclasses import dataclass
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline


EXAMPLES = {
    "billing": ["invoice amount is wrong", "payment was charged twice", "need a fee receipt", "refund has not arrived", "billing address change", "card payment failed"],
    "account": ["cannot reset my password", "account is locked", "change my email address", "login code not received", "update profile name", "delete my account"],
    "technical": ["application crashes on start", "upload button is not working", "page shows a server error", "video does not play", "installation failed", "screen stays blank"],
    "course": ["where is the lesson recording", "when is the next class", "need the course syllabus", "project review schedule", "attendance information", "certificate eligibility"],
}


@dataclass(frozen=True)
class Route:
    queue: str
    confidence: float
    needs_review: bool


def train_router() -> Pipeline:
    texts, labels = [], []
    for label, messages in EXAMPLES.items():
        texts.extend(messages)
        labels.extend([label] * len(messages))
    return Pipeline([("tfidf", TfidfVectorizer(ngram_range=(1, 2))),
                     ("model", LogisticRegression(max_iter=1000, random_state=42))]).fit(texts, labels)


def route_ticket(model: Pipeline, text: str, threshold: float = 0.45) -> Route:
    probability = model.predict_proba([text])[0]
    index = int(probability.argmax())
    confidence = float(probability[index])
    return Route(str(model.classes_[index]), confidence, confidence < threshold)


def main() -> None:
    model = train_router()
    for ticket in ("My card was charged twice", "The upload page crashes", "I have a complicated question"):
        print(ticket, "->", route_ticket(model, ticket))


if __name__ == "__main__":
    main()