facility_predict.py

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.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()
        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"]
        text_single = ' '.join(word for word in text.split() if word not in stopwords)
        return text_single

    def encode_fn(self, text_single):
        """
        Using tokenizer to preprocess the text
        example of text_single:'Nairobi Hospital'
        """
        tokenizer = self.tokenizer(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, text_single):
        """
        Preprocess text and prepare dataloader for a single new sentence
        """
        input_ids, attention_mask = self.encode_fn(text_single)
        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('/content/drive/MyDrive/dhis14000/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)[:1])
        # temp.append(top_results)
        # final_preds = json.dumps(temp)
        final_preds = ', '.join(top_results.keys())
        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
                       )


def predict_batch_from_csv(input_file, output_file):
    # Load batch data from CSV
    batch_data = pd.read_csv(input_file)

    # Initialize predictions list
    predictions = []

    # Iterate over rows with tqdm for progress tracking
    for _, row in tqdm(batch_data.iterrows(), total=len(batch_data)):
        text = row['facility_name']  # Replace 'facility_name' with the actual column name containing the text data
        cleaned_text = processor.clean_text(text)
        prepared_data = processor.process_tokenizer(cleaned_text)
        prediction = obj_Facility_Model.inference(prepared_data)
        predictions.append(prediction)

    # Create DataFrame for predictions
    output_data = pd.DataFrame({'prediction': predictions})
    # Merge with input DataFrame
    pred_output_df = pd.concat([batch_data, output_data], axis=1)
    # Save predictions to CSV
    pred_output_df.to_csv(output_file, index=False)