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import gradio as gr
import torch
from transformers import ViTModel, BertModel, BertTokenizer
from torchvision import transforms
from PIL import Image
import json
from torch import nn
from huggingface_hub import hf_hub_download
# Định nghĩa mô hình
class VQAModel(nn.Module):
def __init__(self, num_answers):
super(VQAModel, self).__init__()
self.vit = ViTModel.from_pretrained('google/vit-base-patch16-224')
self.bert = BertModel.from_pretrained('bert-base-uncased')
self.classifier = nn.Sequential(
nn.Dropout(0.5),
nn.Linear(768 * 3, num_answers)
)
def forward(self, image, input_ids, attention_mask):
image_features = self.vit(image).last_hidden_state[:, 0, :]
text_features = self.bert(input_ids, attention_mask=attention_mask).last_hidden_state[:, 0, :]
combined = torch.cat([image_features, text_features, image_features * text_features], dim=1)
output = self.classifier(combined)
return output
# Load mô hình từ Hugging Face Hub
repo_id = "duyan2803/vqa-model-vilt-bert-color-optim"
device = "cuda" if torch.cuda.is_available() else "cpu"
try:
config_path = hf_hub_download(repo_id=repo_id, filename="config.json")
with open(config_path, "r") as f:
config = json.load(f)
num_answers = config["num_answers"]
weights_path = hf_hub_download(repo_id=repo_id, filename="pytorch_model.bin")
model = VQAModel(num_answers=num_answers)
state_dict = torch.load(weights_path, map_location=device, weights_only=True)
model.load_state_dict(state_dict)
model.to(device)
model.eval()
tokenizer = BertTokenizer.from_pretrained(repo_id)
answer_list_path = hf_hub_download(repo_id=repo_id, filename="answer_list.json")
with open(answer_list_path, "r") as f:
answer_list = json.load(f)
except Exception as e:
print(f"Lỗi khi load mô hình: {str(e)}")
raise e
# Hàm dự đoán
def predict(image, question):
try:
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
image_tensor = transform(image).unsqueeze(0).to(device)
tokenized = tokenizer(question, padding='max_length', truncation=True, max_length=32, return_tensors='pt')
input_ids = tokenized['input_ids'].to(device)
attention_mask = tokenized['attention_mask'].to(device)
with torch.no_grad():
output = model(image_tensor, input_ids, attention_mask)
pred_idx = output.argmax(dim=1).item()
return answer_list[pred_idx]
except Exception as e:
return f"Lỗi khi dự đoán: {str(e)}"
# Giao diện Gradio
interface = gr.Interface(
fn=predict,
inputs=[
gr.Image(type="pil", label="Upload an image"),
gr.Textbox(label="Ask a question")
],
outputs=gr.Textbox(label="Answer"),
title="VQA Demo - Car Recognition",
description="Upload an image of a car and ask a question (e.g., 'What color is this car?' or 'What is this car?')."
)
interface.launch() |