from transformers import pipeline
import torch
from typing import Dict, Any

class EndpointHandler:
    def __init__(self, path=""):
        device = 0 if torch.cuda.is_available() else -1
        torch_d_type = torch.float16 if torch.cuda.is_available() else torch.float32

        self.classifier = pipeline(
            task="text-classification",
            model="abullard1/albert-v2-steam-review-constructiveness-classifier",
            tokenizer="albert-base-v2",
            device=device,
            top_k=None,
            truncation=True,
            max_length=512,
            torch_dtype=torch_d_type
        )

    def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
        input_text = data.get("inputs", "")

        results = self.classifier(input_text)

        label_1, score_1 = results[0][0]["label"], results[0][0]["score"]
        label_2, score_2 = results[0][1]["label"], results[0][1]["score"]

        return {
            "label_1": label_1,
            "score_1": score_1,
            "label_2": label_2,
            "score_2": score_2,
            "prediction_text": self.format_prediction_text(label_1, score_1, label_2, score_2)
        }

    def format_prediction_text(self, label_1, score_1, label_2, score_2) -> str:
        def label_to_constructiveness(label):
            return "Constructive" if label == "LABEL_1" else "Not Constructive"
        
        if score_1 >= score_2:
            return f"{label_to_constructiveness(label_1)} with a score of {score_1:.2f}. 👍🏻"
        else:
            return f"{label_to_constructiveness(label_2)} with a score of {score_2:.2f}. 👎🏻"