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import os
import json
import torch
import numpy as np
from transformers import BertTokenizer
from ts.torch_handler.base_handler import BaseHandler
from model import ImprovedBERTClass
from sklearn.preprocessing import OneHotEncoder
class UICardMappingHandler(BaseHandler):
def __init__(self):
super().__init__()
self.initialized = False
def initialize(self, context):
self.manifest = context.manifest
properties = context.system_properties
model_dir = properties.get("model_dir")
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Load config
with open(os.path.join(model_dir, 'config.json'), 'r') as f:
self.config = json.load(f)
# Initialize encoder and labels
self.labels = ['Videos', 'Unit Conversion', 'Translation', 'Shopping Product Comparison', 'Restaurants', 'Product', 'Information', 'Images', 'Gift', 'General Comparison', 'Flights', 'Answer', 'Aircraft Seat Map']
labels_np = np.array(self.labels).reshape(-1, 1)
self.encoder = OneHotEncoder(sparse_output=False)
self.encoder.fit(labels_np)
# Load model
self.model = ImprovedBERTClass()
self.model.load_state_dict(torch.load(os.path.join(model_dir, 'model.pth'), map_location=self.device))
self.model.to(self.device)
self.model.eval()
# Load tokenizer
self.tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
self.initialized = True
def preprocess(self, data):
text = data[0].get("body").get("text", "")
k = data[0].get("body").get("k", 3)
inputs = self.tokenizer.encode_plus(
text,
add_special_tokens=True,
max_length=64,
padding='max_length',
return_tensors='pt',
truncation=True
)
return {
"ids": inputs['input_ids'].to(self.device, dtype=torch.long),
"mask": inputs['attention_mask'].to(self.device, dtype=torch.long),
"token_type_ids": inputs['token_type_ids'].to(self.device, dtype=torch.long),
"k": k
}
def inference(self, data):
with torch.no_grad():
outputs = self.model(data["ids"], data["mask"], data["token_type_ids"])
probabilities = torch.sigmoid(outputs)
return probabilities.cpu().detach().numpy().flatten(), data["k"]
def postprocess(self, inference_output):
probabilities, k = inference_output
# Get top k predictions
top_k_indices = np.argsort(probabilities)[-k:][::-1]
top_k_probs = probabilities[top_k_indices]
# Create one-hot encodings for top k indices
top_k_one_hot = np.zeros((k, len(probabilities)))
for i, idx in enumerate(top_k_indices):
top_k_one_hot[i, idx] = 1
# Decode the top k predictions
top_k_cards = [self.decode_vector(one_hot.reshape(1, -1)) for one_hot in top_k_one_hot]
# Create a list of tuples (card, probability) for top k predictions
top_k_predictions = list(zip(top_k_cards, top_k_probs.tolist()))
# Determine the most likely card
predicted_labels = (probabilities > 0.5).astype(int)
if sum(predicted_labels) == 0:
most_likely_card = "Answer"
else:
most_likely_card = self.decode_vector(predicted_labels.reshape(1, -1))
# Prepare the response
result = {
"most_likely_card": most_likely_card,
"top_k_predictions": top_k_predictions
}
return [result]
def decode_vector(self, vector):
original_label = self.encoder.inverse_transform(vector)
return original_label[0][0] # Returns the label as a string
def handle(self, data, context):
self.context = context
data = self.preprocess(data)
data = self.inference(data)
data = self.postprocess(data)
return data |