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import datasets
import numpy as np
import torch
import transformers
from config import epochs, batch_size, learning_rate, id2label
from model import tokenizer, multitask_model
from mtm import MultitaskTrainer, NLPDataCollator, DataLoaderWithTaskname
import pandas as pd
from datasets import Dataset, DatasetDict
from data_predict import convert_to_stsb_features,convert_to_features
import gradio as gr
from huggingface_hub import hf_hub_download,snapshot_download
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_link = hf_hub_download(repo_id="FFZG-cleopatra/Croatian-News-Classifier",filename = "pytorch_model.bin")
multitask_model.load_state_dict(torch.load(model_link, map_location=device))
multitask_model.to(device)
def predict(sentence = "Volim ti"):
# gather everyone if you want to have a single DatasetDict
document = DatasetDict({
# "train": Dataset.from_pandas(df_document_sl_hr_train),
# "valid": Dataset.from_pandas(df_document_sl_hr_valid),
"test": Dataset.from_dict({"content":[sentence]})
})
dataset_dict = {
"document": document,
}
for task_name, dataset in dataset_dict.items():
print(task_name)
print(dataset_dict[task_name]["test"][0])
print()
convert_func_dict = {
"document": convert_to_stsb_features,
# "paragraph": convert_to_stsb_features,
# "sentence": convert_to_stsb_features,
}
features_dict = convert_to_features(dataset_dict, convert_func_dict)
return features_dict
predictions = []
features_dict = predict()
for _, batch in enumerate(features_dict["document"]['test']):
for key, value in batch.items():
batch[key] = batch[key].to(device)
task_model = multitask_model.get_model("document")
classifier_output = task_model.forward(
torch.unsqueeze(batch["input_ids"], 0),
torch.unsqueeze(batch["attention_mask"], 0),)
print(tokenizer.decode(batch["input_ids"],skip_special_tokens=True))
prediction =torch.max(classifier_output.logits, axis=1)
predictions.append(prediction.indices.item())
print("p:", predictions[0] , id2label[predictions[0]] )
return id2label[predictions[0]]
interface = gr.Interface(
fn=get_sentiment,
inputs='text',
outputs=['text', 'label'],
title='Sentiment Analysis',
description='Get the positive/neutral/negative sentiment for the given input.'
)
interface.launch(inline = False)
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