Create app.py

app.py

@@ -0,0 +1,45 @@
+import joblib
+import gradio as gr
+from datasets import Dataset, DatasetDict, load_dataset
+
+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
+
+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="text"), 
+                         title="Arabic Text Classifier", description="Classify Arabic text into categories bu using Logistic Regression")
+
+interface.launch()