test_medgemma.py

from transformers import AutoProcessor, AutoModelForImageTextToText
import torch
from PIL import Image
import os

model_id = "google/medgemma-1.5-4b-it"

def test_medgemma():
    print(f"Loading {model_id}...")
    try:
        processor = AutoProcessor.from_pretrained(model_id)
        # We try to load without device_map="auto" for MPS or manual device control
        model = AutoModelForImageTextToText.from_pretrained(
            model_id,
            torch_dtype=torch.float32, # CPU usually stable with float32
            trust_remote_code=True
        ).eval()
        
        print("Model loaded.")
        
        image_path = "sample_test.png"
        if not os.path.exists(image_path):
            print("No test image found.")
            return
            
        image = Image.open(image_path).convert("RGB")
        
        # Use chat template as suggested
        messages = [
            {
                "role": "user",
                "content": [
                    {"type": "image", "image": image},
                    {"type": "text", "text": "Extract all text from this image."}
                ]
            }
        ]
        
        inputs = processor.apply_chat_template(
            messages, 
            add_generation_prompt=True, 
            tokenize=True,
            return_dict=True, 
            return_tensors="pt"
        )
        
        print("Running inference...")
        with torch.no_grad():
            output = model.generate(**inputs, max_new_tokens=100)
            
        input_len = inputs["input_ids"].shape[-1]
        result = processor.decode(output[0][input_len:], skip_special_tokens=True)
        
        print("\n--- MedGemma Result ---")
        print(result)
        print("-----------------------")
        
    except Exception as e:
        print(f"Error: {e}")
        import traceback
        traceback.print_exc()

if __name__ == "__main__":
    test_medgemma()