test_model.py

#!/usr/bin/env python3
"""
Quick test of the trained Prothom Alo model
"""

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

def test_model():
    """Test the fine-tuned model"""
    
    print("🚀 Testing Prothom Alo Fine-tuned Model")
    print("=" * 50)
    
    # Load the fine-tuned model
    model_path = "./prothomalo_model/final_model"
    print(f"Loading model from: {model_path}")
    
    tokenizer = AutoTokenizer.from_pretrained(model_path)
    model = AutoModelForCausalLM.from_pretrained(model_path)
    
    # Test text generation
    prompts = [
        "The latest news from Bangladesh",
        "In today's opinion piece",
        "Government announces new policy"
    ]
    
    for i, prompt in enumerate(prompts, 1):
        print(f"\n🧪 Test {i}: {prompt}")
        print("-" * 40)
        
        # Tokenize input
        inputs = tokenizer(prompt, return_tensors="pt")
        
        # Generate text
        with torch.no_grad():
            outputs = model.generate(
                **inputs,
                max_length=150,
                num_return_sequences=1,
                do_sample=True,
                temperature=0.8,
                pad_token_id=tokenizer.eos_token_id
            )
        
        # Decode and display
        generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
        print(f"Generated: {generated_text}")
    
    # Test Safetensors loading
    print(f"\n🔒 Testing Safetensors Format")
    print("-" * 40)
    
    try:
        from safetensors import safe_open
        
        with safe_open("./prothomalo_model.safetensors", framework="pt", device=0) as f:
            keys = list(f.keys())
            print(f"✅ Safetensors loaded successfully!")
            print(f"📊 Contains {len(keys)} tensors")
            print(f"🔍 First 3 tensor names:")
            for key in keys[:3]:
                print(f"  - {key}")
                
    except Exception as e:
        print(f"❌ Safetensors test failed: {e}")
    
    print(f"\n🎉 Model testing completed!")
    return True

if __name__ == "__main__":
    test_model()