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import gradio as gr
import torch
from transformers import AutoProcessor, AutoModelForCausalLM
from huggingface_hub import hf_hub_download
from PIL import Image
from datasets import load_dataset
processor = AutoProcessor.from_pretrained("microsoft/git-base-vqav2")
model = AutoModelForCausalLM.from_pretrained("microsoft/git-base-vqav2")
dataset = load_dataset("question","answers","image")
file_path = hf_hub_download(repo_id="Multimodal-Fatima/OK-VQA_train", repo_type="dataset")
image = Image.open(file_path).convert("RGB")
pixel_values = processor(images=image, return_tensors="pt").pixel_values
input_ids = processor(text=question, add_special_tokens=False).input_ids
input_ids = [processor.tokenizer.cls_token_id] + input_ids
input_ids = torch.tensor(input_ids).unsqueeze(0)
generated_ids = model.generate(pixel_values=pixel_values, input_ids=input_ids, max_length=50)
print(processor.batch_decode(generated_ids, skip_special_tokens=True)) |