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import os
import pickle
import gradio as gr
from transformers import AutoModel, AutoTokenizer
from .utils import extract_hidden_state
# Load model
models_dir = os.path.join(os.path.dirname(__file__), '..', 'models')
model_file = os.path.join(models_dir, 'logistic_regression.pkl')
if os.path.exists(model_file):
with open(model_file, "rb") as f:
model = pickle.load(f)
else:
print(f"Error: {model_file} not found.")
# Load html
html_dir = os.path.join(os.path.dirname(__file__), "templates")
index_html_path = os.path.join(html_dir, "index.html")
if os.path.exists(index_html_path):
with open(index_html_path, "r") as html_file:
index_html = html_file.read()
else:
print(f"Error: {index_html_path} not found.")
# Load pre-trained model
model_name = "moussaKam/AraBART"
tokenizer = AutoTokenizer.from_pretrained(model_name)
language_model = AutoModel.from_pretrained(model_name)
def classify_arabic_dialect(text):
text_embeddings = extract_hidden_state(text, tokenizer, language_model)
predicted_class = model.predict(text_embeddings)[0]
return predicted_class
with gr.Blocks() as demo:
gr.HTML(index_html)
input_text = gr.Textbox(label="Your Arabic Text")
submit_btn = gr.Button("Submit")
with gr.Row():
first_country = gr.Textbox()
second_country = gr.Textbox()
third_country = gr.Textbox()
submit_btn.click(
fn=classify_arabic_dialect,
inputs=input_text,
outputs=[first_country, second_country, third_country])
gr.HTML("<p>Checkout the Github Repo:</p>")
if __name__ == "__main__":
demo.launch()
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