Update app.py

app.py

@@ -3,45 +3,52 @@ from transformers import pipeline
 import requests
 import json
 
-# اسم النموذج
+# Model name on Hugging Face
 model_name = "Woolv7007/egyptian-text-classification"
 
-# تحميل labels.json
+# Load label names from labels.json
 labels_url = f"https://huggingface.co/{model_name}/resolve/main/labels.json"
 try:
     response = requests.get(labels_url)
     response.raise_for_status()
     labels = response.json()
+
+    # Convert to list if it's a dictionary
     if isinstance(labels, dict):
         labels = list(labels.values())
-    print("✅ Labels loaded:", labels)
+
+    print("Labels loaded:", labels)
+
 except requests.exceptions.RequestException as e:
-    print("❌ فشل تحميل labels.json:", e)
+    print("Failed to load labels.json:", e)
     labels = None
 
-# تحميل النموذج
+# Load the text classification pipeline
 pipe = pipeline("text-classification", model=model_name)
-print("✅ Model pipeline loaded.")
+print("Model pipeline loaded.")
 
-# دالة التنبؤ
+# Define the prediction function
 def predict(text):
-    print("🎯 Received:", text)
+    print("Input text:", text)
+
     try:
+        # Run prediction
         result = pipe(text)[0]
-        print("⚙️ Pipeline result:", result)
+        print("Model output:", result)
 
-        # استخراج رقم التصنيف
+        # Extract label index from format like "LABEL_3"
         label_id = int(result['label'].replace("LABEL_", ""))
-        print("🔢 Label ID:", label_id)
+        print("Label ID:", label_id)
 
-        # استخراج الاسم من labels
+        # Get the label name using the index
         label_text = labels[label_id] if labels and label_id < len(labels) else result['label']
-        print("🏷️ Final label:", label_text)
+        print("Final label:", label_text)
 
-        # الثقة
+        # Get the confidence score
         confidence = round(result['score'], 3)
-        print("📊 Confidence:", confidence)
+        print("Confidence:", confidence)
 
+        # Build JSON result
         json_output = {
             "prediction": label_text,
             "confidence": confidence
@@ -50,18 +57,17 @@ def predict(text):
         return label_text, json.dumps(json_output, indent=4, ensure_ascii=False)
 
     except Exception as e:
-        err_msg = str(e)
-        print("❌ Error in predict:", err_msg)
-        return "Error", json.dumps({"error": err_msg}, indent=4, ensure_ascii=False)
+        print("Error:", e)
+        return "Error", json.dumps({"error": str(e)}, indent=4, ensure_ascii=False)
 
-# واجهة Gradio
+# Create the Gradio interface
 gr.Interface(
     fn=predict,
-    inputs=gr.Textbox(lines=3, placeholder="اكتب جملة باللهجة المصرية..."),
+    inputs=gr.Textbox(lines=3, placeholder="Enter a sentence in Egyptian Arabic..."),
     outputs=[
-        gr.Label(label="التصنيف المتوقع"),
-        gr.Textbox(label="النتيجة بصيغة JSON")
+        gr.Label(label="Predicted Category"),
+        gr.Textbox(label="JSON Output")
     ],
-    title="🧠 تصنيف النصوص باللهجة المصرية",
-    description="🔍 نموذج يصنف النصوص إلى: حياد، إساءة، عنصرية، تمييز ديني، إعلانات، إلخ."
+    title="Egyptian Arabic Text Classifier",
+    description="This model classifies Egyptian Arabic text into categories such as Neutral, Offensive, Racist, Religious Discrimination, Ads, etc."
 ).launch()