| import gradio as gr | |
| import joblib | |
| import os | |
| model = joblib.load('arabic_text_classifier.pkl') | |
| vectorizer = joblib.load('tfidf_vectorizer.pkl') | |
| label_encoder = joblib.load('label_encoder.pkl') | |
| 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 | |
| HF_TOKEN = os.getenv("classification") | |
| hf_writer = gr.HuggingFaceDatasetSaver(HF_TOKEN, "crowdsourced-text-classification-data") | |
| iface = gr.Interface( | |
| fn=predict_category, | |
| inputs=gr.Textbox( | |
| lines=5, | |
| placeholder="Enter text in Arabic here...", | |
| label="Text" | |
| ), | |
| outputs=gr.Label(label="Predicted Category"), | |
| title="Arabic Text Classification", | |
| description="Enter an Arabic text to get its classification based on the trained model.", | |
| allow_flagging="auto", | |
| flagging_callback=hf_writer, | |
| ) | |
| iface.launch(share=True) | |