app.py

#허깅페이스에서 돌아갈 수 있도록 바꾸어 보았음
import torch
from transformers import BertTokenizerFast, BertForQuestionAnswering, Trainer, TrainingArguments
from datasets import load_dataset
from collections import defaultdict

# 데이터 불러오기
dataset_load = load_dataset('Multimodal-Fatima/OK-VQA_train')
dataset = dataset_load['train'].select(range(300))

# 불필요한 특성 선택
selected_features = ['image', 'answers', 'question']
selected_dataset = dataset.map(lambda ex: {feature: ex[feature] for feature in selected_features})

# 소프트 인코딩
answers_to_id = defaultdict(lambda: len(answers_to_id))
selected_dataset = selected_dataset.map(lambda ex: {
    'answers': [answers_to_id[ans] for ans in ex['answers']],
    'question': ex['question'],
    'image': ex['image']
})

id_to_answers = {v: k for k, v in answers_to_id.items()}
id_to_labels = {k: ex['answers'] for k, ex in enumerate(selected_dataset)}

selected_dataset = selected_dataset.map(lambda ex: {'answers': id_to_labels.get(ex['answers'][0]),
                                                   'question': ex['question'],
                                                   'image': ex['image']})

flattened_features = []

for ex in selected_dataset:
    flattened_example = {
        'answers': ex['answers'],
        'question': ex['question'],
        'image': ex['image'],
    }
    flattened_features.append(flattened_example)

# 모델 가져오기
from transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer

model_name = 'microsoft/git-base-vqav2'
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Trainer를 사용하여 모델 학습
tokenizer = BertTokenizerFast.from_pretrained('bert-base-multilingual-cased')

def preprocess_function(examples):
    tokenized_inputs = tokenizer(examples['question'], truncation=True, padding=True)
    return {
        'input_ids': tokenized_inputs['input_ids'],
        'attention_mask': tokenized_inputs['attention_mask'],
        'pixel_values': [(4, 3, 244, 244)] * len(tokenized_inputs['input_ids']),
        'pixel_mask': [1] * len(tokenized_inputs['input_ids']),
        'labels': [[label] for label in examples['answers']]
    }

dataset = load_dataset("Multimodal-Fatima/OK-VQA_train")['train'].select(range(300))
ok_vqa_dataset = dataset.map(preprocess_function, batched=True)
ok_vqa_dataset.set_format(type='torch', columns=['input_ids', 'attention_mask', 'pixel_values', 'pixel_mask', 'labels'])

training_args = TrainingArguments(
    output_dir='./results',
    num_train_epochs=20,
    per_device_train_batch_size=4,
    logging_steps=500,
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=ok_vqa_dataset
)

# 모델 학습
trainer.train()


import gradio as gr
import torch
from transformers import BertTokenizer, BertForSequenceClassification

# 모델 초기화 및 가중치 불러오기
model_name = 'microsoft/git-base-vqav2'  # 사용할 모델의 이름
model = BertForSequenceClassification.from_pretrained(model_name)
tokenizer = BertTokenizer.from_pretrained(model_name)

# 예측 함수 정의
def predict_answer(image, question):
    inputs = tokenizer(question, return_tensors='pt')
    input_ids = inputs['input_ids']
    attention_mask = inputs['attention_mask']

    # 이미지와 관련된 처리 수행
    # 이미지 처리 코드를 여기에 추가해야 합니다 (입력된 이미지에 대한 전처리 등)

    # 모델에 입력 데이터를 전달하여 예측 수행
    with torch.no_grad():
        outputs = model(input_ids=input_ids, attention_mask=attention_mask)

    # 예측 결과에서 가장 높은 확률을 가진 레이블 ID 가져오기
    predicted_label_id = torch.argmax(outputs.logits).item()
    predicted_label = id_to_label_fn(predicted_label_id)

    return predicted_label

iface = gr.Interface(
    fn=predict_answer,
    inputs=["image", "text"],
    outputs="text",
    title="Visual Question Answering",
    description="Input an image and a question to get the model's answer.",
    example=[
        "https://your_image_url.jpg",
        "What is shown in the image?"
    ]
)

iface.launch()