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import streamlit as st
from typing import Any, Dict, Optional
import numpy as np
import torch
from transformers import AutoTokenizer
from transformers import AutoModelForSequenceClassification
from libs.normalizer import Normalizer
from libs.languages import languages
from libs.examples import EXAMPLES
from libs.dummy import outputs as dummy_outputs
from libs.utils import plot_result
import meta
class Zabanshenas:
def __init__(
self,
model_name_or_path: str = "m3hrdadfi/zabanshenas-roberta-base-mix",
by_gpu: bool = False
) -> None:
self.debug = False
self.dummy_outputs = dummy_outputs
self.device = torch.device("cpu" if not by_gpu else "cuda")
self.model_name_or_path = model_name_or_path
self.tokenizer = None
self.model = None
self.normalizer = None
self.languages = None
self.framework = "pt"
self.max_length = 512
self.top_k = 5
def load(self):
if not self.debug:
self.tokenizer = AutoTokenizer.from_pretrained(self.model_name_or_path)
self.model = AutoModelForSequenceClassification.from_pretrained(self.model_name_or_path).to(self.device)
self.normalizer = Normalizer()
self.languages = languages
def ensure_tensor_on_device(self, **inputs):
"""
Ensure PyTorch tensors are on the specified device.
"""
return {
name: tensor.to(self.device) if isinstance(tensor, torch.Tensor) else tensor
for name, tensor in inputs.items()
}
def _parse_and_tokenize(
self,
inputs,
do_normalization: bool = True,
max_length: int = 512,
padding: bool = True,
add_special_tokens: bool = True,
truncation: bool = True,
):
"""
Parse arguments and tokenize
"""
inputs = [self.normalizer(item) for item in inputs]
max_length = min(max_length, self.max_length)
inputs = self.tokenizer(
inputs,
max_length=max_length,
add_special_tokens=add_special_tokens,
return_tensors=self.framework,
padding=padding,
truncation=truncation,
)
return inputs
def _forward(
self,
inputs,
return_tensors: bool = True
):
with torch.no_grad():
inputs = self.ensure_tensor_on_device(**inputs)
predictions = self.model(**inputs)[0].cpu()
if return_tensors:
return predictions
else:
return predictions.numpy()
def detect(
self,
texts,
max_length: int = 128,
do_normalization: bool = True
):
if self.debug:
return self.dummy_outputs
texts = [texts] if not isinstance(texts, list) else texts
inputs = self._parse_and_tokenize(texts, do_normalization=do_normalization, max_length=max_length)
outputs = self._forward(inputs, return_tensors=False)
scores = np.exp(outputs) / np.exp(outputs).sum(-1, keepdims=True)
results = [
[
{
"language": self.languages.get(self.model.config.id2label[i], None),
"code": self.model.config.id2label[i],
"score": score.item()
} for i, score in enumerate(item)
] for item in scores
]
results = [list(sorted(result, key=lambda kv: kv["score"], reverse=True)) for result in results]
return results
@st.cache(allow_output_mutation=True)
def load_language_detector():
detector = Zabanshenas()
detector.load()
return detector
def main():
st.set_page_config(
page_title="Zabanshenas",
page_icon="🕵",
layout="wide",
initial_sidebar_state="expanded"
)
detector = load_language_detector()
col1, col2 = st.beta_columns([6, 4])
with col2:
st.markdown(meta.INFO, unsafe_allow_html=True)
with col1:
prompts = list(EXAMPLES.keys()) + ["Custom"]
prompt = st.selectbox(
'Examples (select from this list)',
prompts,
# index=len(prompts) - 1,
index=0
)
if prompt == "Custom":
prompt_box = ""
else:
prompt_box = EXAMPLES[prompt]
text = st.text_area(
'Insert your text: ',
detector.normalizer(prompt_box),
height=200
)
text = detector.normalizer(text)
entered_text = st.empty()
detect_language = st.button('Detect Language !')
st.markdown(
"<hr />",
unsafe_allow_html=True
)
if detect_language:
words = text.split()
with st.spinner("Detecting..."):
if not len(words) > 3:
entered_text.markdown(
"Insert your text (at least three words)"
)
else:
top_languages = detector.detect(text, max_length=min(len(words), detector.max_length))
top_languages = top_languages[0][:detector.top_k]
plot_result(top_languages)
if __name__ == '__main__':
main()
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