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import torch
from transformers import DistilBertForSequenceClassification
import os
# # Get the directory path of the current script
# script_dir = os.path.dirname(os.path.abspath(__file__))
# model = DistilBertForSequenceClassification.from_pretrained("model.safetensors")
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("lxs1/DistilBertForSequenceClassification_6h_768dim")
model = AutoModelForSequenceClassification.from_pretrained("lxs1/DistilBertForSequenceClassification_6h_768dim")
# from transformers import DistilBertTokenizerFast
# tokenizer = DistilBertTokenizerFast.from_pretrained('distilbert-base-uncased')
# Move the model to the GPU if available
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)
def sentiment_class(summarized_text):
'''
# 1 = non-depressed
# 0 = depressed
returns: example:- array([[0.00493283, 0.9950671 ]], dtype=float32)
'''
#inputs = tokenizer(summarized_text, padding = True, truncation = True, return_tensors='pt').to('cuda')
inputs = tokenizer(summarized_text, padding = True, truncation = True, return_tensors='pt').to(device)
outputs = model(**inputs)
predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
predictions = predictions.cpu().detach().numpy()
return predictions
def pattern_classification():
result=""
return result
def corelation_analysis():
result=""
return result |