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import joblib
import gradio as gr
from datasets import Dataset, DatasetDict, load_dataset
from huggingface_hub import login
import os
token = os.getenv('HF_TOKEN')
login(token, add_to_git_credential=True,write_permission=True )
model = joblib.load('arabic_text_classifier.pkl')
vectorizer = joblib.load('tfidf_vectorizer.pkl')
label_encoder = joblib.load('label_encoder.pkl')
available_labels = label_encoder.classes_
def predict_category(text):
text_vector = vectorizer.transform([text])
probabilities = model.predict_proba(text_vector)[0]
max_prob = max(probabilities)
predicted_category = model.predict(text_vector)[0]
if max_prob < 0.5:
return "Other"
predicted_label = label_encoder.inverse_transform([predicted_category])[0]
return predicted_label
def flag_data(text, prediction):
try:
dataset = load_dataset("Tevfik34/crowdsourced-text-classification-data", split="train")
except:
dataset = Dataset.from_dict({"text": [], "prediction": []})
new_data = {"text": [text], "prediction": [prediction]}
dataset = dataset.add_item(new_data)
dataset.push_to_hub("Tevfik34/crowdsourced-text-classification-data")
def classify_and_flag(text):
prediction = predict_category(text)
flag_data(text, prediction)
return prediction
interface = gr.Interface(fn=classify_and_flag,
inputs=gr.Textbox(lines=5, placeholder= "Enter text in Arabic here...", label="Text" ),
outputs=gr.Label(label="Predicted Category"),
title="Arabic Text Classifier",
description="""
This interface allows you to classify Arabic text into different categories using a machine learning model trained on 160,000 real-world text samples.
**Model Overview**:
- The model is based on **Logistic Regression**.
- It was trained on a large dataset of **160,000 Arabic text entries**, ensuring robustness and accuracy in classifying Arabic text.
**How to use**:
- Enter any Arabic text in the input box.
- The model will predict the category that the text most likely belongs to.
- If the model is uncertain, it will classify the text as 'Other'.
**Available Labels**:
The model can predict the following categories:
- {}
Try entering some text in Arabic to see how the model works.
""".format(", ".join(available_labels)),theme="ParityError/Interstellar")
interface.launch() |