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from transformers import AutoProcessor, AutoModelForImageTextToText |
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from PIL import Image |
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import requests |
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import torch |
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model_id = "google/medgemma-4b-it" |
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model = AutoModelForImageTextToText.from_pretrained( |
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model_id, |
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torch_dtype=torch.bfloat16, |
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device_map="auto", |
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) |
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processor = AutoProcessor.from_pretrained(model_id) |
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image_url = "https://upload.wikimedia.org/wikipedia/commons/c/c8/Chest_Xray_PA_3-8-2010.png" |
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image = Image.open(requests.get(image_url, headers={"User-Agent": "example"}, stream=True).raw) |
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messages = [ |
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{ |
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"role": "system", |
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"content": [{"type": "text", "text": "You are an expert radiologist."}] |
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}, |
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{ |
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"role": "user", |
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"content": [ |
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{"type": "text", "text": "Describe this X-ray"}, |
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{"type": "image", "image": image} |
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] |
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} |
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] |
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inputs = processor.apply_chat_template( |
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messages, add_generation_prompt=True, tokenize=True, |
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return_dict=True, return_tensors="pt" |
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).to(model.device, dtype=torch.bfloat16) |
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input_len = inputs["input_ids"].shape[-1] |
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with torch.inference_mode(): |
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generation = model.generate(**inputs, max_new_tokens=200, do_sample=False) |
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generation = generation[0][input_len:] |
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decoded = processor.decode(generation, skip_special_tokens=True) |
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print(decoded) |
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