handler.py

import os
import json
import torch
import pickle
from transformers import BertForSequenceClassification, BertTokenizer

class CustomPipeline:
    def __init__(self, model_path):
        # Load the model and tokenizer from the provided path
        self.model = BertForSequenceClassification.from_pretrained(model_path)
        self.tokenizer = BertTokenizer.from_pretrained(model_path)
        
        # Load the label encoder
        pkl_path = os.path.join(model_path, 'label_encoder.pkl')
        # pkl_path = os.path.abspath('label_encoder.pkl')
        with open(pkl_path, 'rb') as le_file:
            self.le = pickle.load(le_file)

def __call__(self, inputs):
    # Tokenize the input
    tokenized_inputs = self.tokenizer(inputs, return_tensors='pt', truncation=True, padding=True)
    
    # Get the model's predictions
    with torch.no_grad():
        logits = self.model(**tokenized_inputs).logits
    
    # Convert logits to probabilities
    probs = logits.softmax(dim=1)
    
    # Get the predicted label and confidence
    predicted_class = torch.argmax(probs, dim=1).item()
    confidence = probs[0][predicted_class].item()
    round_confidence = round(float(confidence), 2)
    label = self.le.inverse_transform([predicted_class])[0]
    
    return {"label": label, "confidence": round_confidence, "metadata": "Bert-2023-10"}



# Initialize the pipeline once (to avoid reloading the model on every invocation)
MODEL_PATH = "parvashah/bert-product-cat" # You can set an environment variable in Lambda
pipeline = CustomPipeline(MODEL_PATH)

def lambda_handler(event, context):
    try:
        # Extract the input text from the event (assuming the input comes as {"text": "your input here"})
        text_input = event["body"]["text"]

        # Use the pipeline to get the prediction
        result = pipeline(text_input)

        return {
            "statusCode": 200,
            "body": json.dumps(result),
            "headers": {
                "Content-Type": "application/json"
            }
        }

    except Exception as e:
        return {
            "statusCode": 500,
            "body": json.dumps({"error": str(e)}),
            "headers": {
                "Content-Type": "application/json"
            }
        }