Update app.py

app.py

@@ -1,36 +1,101 @@
-import gradio as gr
-import joblib
-
-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
-
-
-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.", 
-)
-
-
-iface.launch(share=True)
+import gradio as gr
+import joblib
+
+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
+
+
+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.", 
+)
+
+
+iface.launch(share=True)
+
+
+import gradio as gr
+import joblib
+import os
+import csv
+from datetime import datetime
+
+
+model = joblib.load('arabic_text_classifier.pkl')
+vectorizer = joblib.load('tfidf_vectorizer.pkl')
+label_encoder = joblib.load('label_encoder.pkl')
+
+
+LOG_FILE = "predictions_log.csv"
+
+
+def log_prediction(input_text, predicted_label):
+    
+    file_exists = os.path.isfile(LOG_FILE)
+
+    
+    with open(LOG_FILE, mode='a', newline='', encoding='utf-8') as file:
+        writer = csv.writer(file)
+
+        
+        if not file_exists:
+            writer.writerow(["Timestamp", "User Input", "Predicted Category"])
+
+        
+        writer.writerow([datetime.now().strftime("%Y-%m-%d %H:%M:%S"), input_text, predicted_label])
+
+
+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:
+        predicted_label = "Other"  
+    else:
+        predicted_label = label_encoder.inverse_transform([predicted_category])[0]
+
+    # Log the prediction
+    log_prediction(text, predicted_label)
+
+    # Return the predicted label
+    return predicted_label
+
+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.", 
+)
+
+
+iface.launch()