Upload example_usage.py

example_usage.py

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+#!/usr/bin/env python3
+"""
+Example script to test the Arabic Message Classification Model
+"""
+
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
+import torch
+
+def main():
+    # Model name - replace with your actual model name on Hugging Face
+    model_name = "ahmedmajid92/Arabic_MI_Classifier"
+    
+    print("Loading Arabic Message Classification Model...")
+    print(f"Model: {model_name}")
+    
+    try:
+        # Load tokenizer and model
+        tokenizer = AutoTokenizer.from_pretrained(model_name)
+        model = AutoModelForSequenceClassification.from_pretrained(model_name)
+        
+        # Create classification pipeline
+        device = 0 if torch.cuda.is_available() else -1
+        classifier = pipeline(
+            "text-classification",
+            model=model,
+            tokenizer=tokenizer,
+            device=device
+        )
+        
+        print(f"Model loaded successfully!")
+        print(f"Using device: {'GPU' if device >= 0 else 'CPU'}")
+        print("-" * 50)
+        
+        # Test examples
+        test_examples = [
+            "السلام عليكم ورحمة الله وبركاته",  # greeting
+            "هلو شلونك اليوم؟",                    # greeting + question
+            "متى يبدأ الاجتماع؟",                  # question
+            "عندي مشكلة بالانترنت",                # complaint
+            "أحب القراءة والكتابة",                # general
+            "الكهرباء نفطت",                      # complaint (Iraqi)
+            "شنو الأخبار؟",                       # question (Iraqi)
+            "تحية طيبة",                         # greeting
+            "أعمل مهندساً في شركة تقنية",          # general
+            "الطابعة ما تطبع"                     # complaint (Iraqi)
+        ]
+        
+        print("Testing with example messages:")
+        print("=" * 60)
+        
+        for i, text in enumerate(test_examples, 1):
+            result = classifier(text)[0]
+            label = result['label']
+            confidence = result['score']
+            
+            print(f"{i:2d}. Text: {text}")
+            print(f"    → Label: {label}")
+            print(f"    → Confidence: {confidence:.4f}")
+            print()
+        
+        print("=" * 60)
+        print("Interactive mode - Enter your own text (or 'quit' to exit):")
+        
+        while True:
+            user_input = input("\nEnter Arabic text: ").strip()
+            
+            if user_input.lower() in ['quit', 'exit', 'q']:
+                print("Goodbye!")
+                break
+                
+            if not user_input:
+                continue
+                
+            try:
+                result = classifier(user_input)[0]
+                label = result['label']
+                confidence = result['score']
+                
+                print(f"→ Label: {label}")
+                print(f"→ Confidence: {confidence:.4f}")
+                
+            except Exception as e:
+                print(f"Error processing text: {e}")
+                
+    except Exception as e:
+        print(f"Error loading model: {e}")
+        print("Make sure to:")
+        print("1. Install required packages: pip install transformers torch")
+        print("2. Update the model_name variable with your actual model name")
+        print("3. Check your internet connection")
+
+if __name__ == "__main__":
+    main()