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import os
import random
import json
import numpy as np
import torch
import heapq
import pandas as pd
from tqdm import tqdm
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from torch.utils.data import TensorDataset, DataLoader
import os
import random
import json
import numpy as np
import torch
import heapq
import pandas as pd
from tqdm import tqdm
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from torch.utils.data import TensorDataset, DataLoader
class Preprocess:
def __init__(self, tokenizer_vocab_path, tokenizer_max_len):
self.stopwords = ["i", "was", "transferred",
"from", "to", "nilienda", "kituo",
"cha", "lakini", "saa", "hii", "niko",
"at", "nilienda", "nikahudumiwa", "pole",
"deliver", "na", "ni", "baada", "ya",
"kutumwa", "kutoka", "nilienda",
"ndipo", "nikapewa", "hiyo", "lindam ama", "nikawa",
"mgonjwa", "nikatibiwa", "in", "had", "a",
"visit", "gynaecologist", "ndio",
"karibu", "mimi", "niko", "sehemu", "hospitali",
"serikali", "delivered", "katika", "kaunti", "kujifungua",
"katika", "huko", "nilipoenda", "kwa", "bado", "naedelea",
"sija", "maliza", "mwisho",
"nilianza", "kliniki", "yangu",
"nilianzia", "nilijifungua"]
self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_vocab_path,
use_auth_token='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')
self.max_len = tokenizer_max_len
def clean_text(self, text):
text = text.lower()
self.text_single = ' '.join(word for word in text.split() if word not in self.stopwords)
return self.text_single
def encode_fn(self):
"""
Using tokenizer to preprocess the text
example of text_single:'Nairobi Hospital'
"""
tokenizer = self.tokenizer(self.text_single,
padding=True,
truncation=True,
max_length=self.max_len,
return_tensors='pt'
)
input_ids = tokenizer['input_ids']
attention_mask = tokenizer['attention_mask']
return input_ids, attention_mask
def process_tokenizer(self, data):
"""
Preprocess text and prepare dataloader for a single new sentence
"""
self.clean_text(data)
input_ids, attention_mask = self.encode_fn()
data = TensorDataset(input_ids, attention_mask)
return data
class Facility_Model:
def __init__(self, facility_model_path: any,
max_len: int):
self.max_len = max_len
self.softmax = torch.nn.Softmax(dim=1)
self.gpu = False
self.model = AutoModelForSequenceClassification.from_pretrained(facility_model_path,
use_auth_token='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')
self.model.eval() # set pytorch model for inference mode
if torch.cuda.device_count() > 1:
self.model = torch.nn.DataParallel(self.model)
if self.gpu:
seed = 42
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.deterministic = True
self.device = torch.device('cuda')
else:
self.device = 'cpu'
self.model = self.model.to(self.device)
def predict_single(self, model, pred_data):
"""
Model inference for new single sentence
"""
pred_dataloader = DataLoader(pred_data, batch_size=10, shuffle=False)
for i, batch in enumerate(pred_dataloader):
with torch.no_grad():
outputs = model(input_ids=batch[0].to(self.device),
attention_mask=batch[1].to(self.device)
)
loss, logits = outputs.loss, outputs.logits
probability = self.softmax(logits)
probability_list = probability.detach().cpu().numpy()
return probability_list
def output_intent_probability(self, pred: any) -> dict:
"""
convert the model output into a dictionary with all intents and its probability
"""
output_dict = {}
# transform the relation table(between label and intent)
path_table = pd.read_csv('dhis_label_relation_14357.csv')
label_intent_dict = path_table[["label", "corresponding_label"]].set_index("corresponding_label").to_dict()[
'label']
# transform the output into dictionary(between intent and probability)
for intent in range(pred.shape[1]):
output_dict[label_intent_dict[intent]] = pred[0][intent]
return output_dict
def inference(self, prepared_data):
"""
Make predictions on one new sentence and output a JSON format variable
"""
temp = []
prob_distribution = self.predict_single(self.model, prepared_data)
prediction_results = self.output_intent_probability(prob_distribution.astype(float))
# Filter out predictions containing "dental" or "optical" keywords
filtered_results = {intent: prob for intent, prob in prediction_results.items()
if
"dental" not in intent.lower() and "optical" not in intent.lower() and "eye" not in intent.lower()}
sorted_pred_intent_results = sorted(filtered_results.items(), key=lambda x: x[1], reverse=True)
sorted_pred_intent_results_dict = dict(sorted_pred_intent_results)
# Return the top result
top_results = dict(list(sorted_pred_intent_results)[:4])
temp.append(top_results)
final_preds = json.dumps(temp)
#final_preds = ', '.join(top_results.keys())
#final_preds = ', '.join(top_results)
# final_preds = final_preds.replace("'", "")
return final_preds
jacaranda_hugging_face_model = "Jacaranda/dhis_14000_600k_Test_Model"
obj_Facility_Model = Facility_Model(facility_model_path=jacaranda_hugging_face_model,
max_len=128
)
processor = Preprocess(tokenizer_vocab_path=jacaranda_hugging_face_model,
tokenizer_max_len=128
) |