import gradio as gr
from transformers import AutoModelForVision2Seq, AutoProcessor, AutoModelForVision2Seq, BitsAndBytesConfig
import torch

quantization_config = BitsAndBytesConfig(
    load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_use_double_quant=True, bnb_4bit_compute_dtype=torch.float16
)

processor = AutoProcessor.from_pretrained("HuggingFaceM4/idefics2-8b")
model = AutoModelForVision2Seq.from_pretrained(
    "HuggingFaceM4/idefics2-8b",
    torch_dtype=torch.float16,
    quantization_config=quantization_config,
)


def respond(multimodal_input):
    images = multimodal_input["files"]
    content = [{"type": "image"} for _ in images]
    content.append({"type": "text", "text": multimodal_input["text"]})
    messages = [{"role": "user", "content": content}]
    prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
    inputs = processor(text=prompt, images=[images], return_tensors="pt")
    inputs = {k: v.to(model.device) for k, v in inputs.items()}
    num_tokens = len(inputs["input_ids"][0])
    with torch.inference_mode():
        generated_ids = model.generate(**inputs, max_new_tokens=500)

    new_tokens = generated_ids[:, num_tokens:]
    generated_text = processor.batch_decode(new_tokens, skip_special_tokens=True)[0]
    return generated_text


gr.Interface(respond, inputs=[gr.MultimodalTextbox(file_types=["image"], show_label=False)], outputs="text").launch()