import gradio as gr
from transformers import AutoProcessor, Idefics3ForConditionalGeneration, image_utils
import torch
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
print(f"Using device: {device}")
model_id="eltorio/IDEFICS3_ROCO"
# model = AutoModelForImageTextToText.from_pretrained(model_id).to(device)
base_model_path="HuggingFaceM4/Idefics3-8B-Llama3" #or change to local path
processor = AutoProcessor.from_pretrained(base_model_path, trust_remote_code=True)
model = Idefics3ForConditionalGeneration.from_pretrained(
        base_model_path, torch_dtype=torch.bfloat16
    ).to(device)

model.load_adapter(model_id,device_map="auto")

def infere(image):
    messages = [
        {
        "role": "system",
        "content": [
            {"type": "text", "text": "You are a valuable medical doctor and you are looking at an image of your patient."},
        ]
        },
    {
        "role": "user",
        "content": [
            {"type": "image"},
            {"type": "text", "text": "What do we see in this image?"},
        ]
    },
    ]
    prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
    inputs = processor(text=prompt, images=[image], return_tensors="pt")
    # print(f"inputs: {inputs}")
    inputs = {k: v.to(device) for k, v in inputs.items()}
    generated_ids = model.generate(**inputs, max_new_tokens=100)
    generated_texts = processor.batch_decode(generated_ids, skip_special_tokens=True)
    return generated_texts

radiotest = gr.Interface(fn=infere, inputs="image", outputs="text")
radiotest.launch()