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import requests
from PIL import Image, UnidentifiedImageError
from transformers import AutoTokenizer, AutoImageProcessor, VisionEncoderDecoderModel
import gradio as gr
# Load the model, tokenizer, and image processor with error handling
def load_model_and_components(model_name):
try:
model = VisionEncoderDecoderModel.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
image_processor = AutoImageProcessor.from_pretrained(model_name)
return model, tokenizer, image_processor
except Exception as e:
raise RuntimeError(f"Error loading model components: {e}")
current_model_name = "laicsiifes/swin-distilbertimbau"
model, tokenizer, image_processor = load_model_and_components(current_model_name)
# Function to process the image and generate a caption
def generate_caption(image):
try:
pixel_values = image_processor(image, return_tensors="pt").pixel_values
generated_ids = model.generate(pixel_values)
caption = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
return caption
except Exception:
return "Please upload a valid image."
# Predefined images for selection
predefined_images_urls = [
"http://images.cocodataset.org/val2014/COCO_val2014_000000458153.jpg",
"http://images.cocodataset.org/val2014/COCO_val2014_000000000074.jpg"
]
# Preload PIL.Image objects for predefined images with error handling
predefined_images = []
for url in predefined_images_urls:
try:
predefined_images.append(Image.open(requests.get(url, stream=True).raw))
except Exception as e:
print(f"Error loading predefined image from {url}: {e}")
# Gradio app
def app(image=None, predefined_image=None):
try:
if predefined_image is not None:
image = predefined_image
elif image is None:
return "Please upload a valid image."
return generate_caption(image)
except Exception:
return "Please upload a valid image."
# Define UI
with gr.Blocks() as interface:
gr.Markdown("""
# Welcome to the LAICSI-IFES space for Vision Encoder-Decoder (VED) demonstration
### Be patient with the Swin-GPorTuguese-2 as it is heavier than the Swin-DistilBERTimbau.
""")
with gr.Row():
with gr.Column():
model_selector = gr.Dropdown(choices=["laicsiifes/swin-distilbertimbau", "laicsiifes/swin-gportuguese-2"],
value="laicsiifes/swin-distilbertimbau",
label="Select Model")
loading_message = gr.Textbox(label="Status Message")
image_display = gr.Image(type="pil", label="Image Preview", interactive=False)
upload_button = gr.File(label="Upload an Image", file_types=["image"], type="filepath")
predefined_images_display = gr.Gallery(predefined_images_urls, label="Choose a Predefined Image")
with gr.Column():
output_text = gr.Textbox(label="Generated Caption")
# Define logic
def handle_uploaded_image(image):
try:
if image is None:
return None, "Please upload a valid image."
pil_image = Image.open(image)
return pil_image, generate_caption(pil_image)
except Exception:
return None, "Please upload a valid image."
def handle_predefined_image(evt: gr.SelectData, _):
try:
if not evt:
return None, "Please upload a valid image."
pil_image = Image.open(requests.get(evt.value['image']['url'], stream=True).raw)
return pil_image, generate_caption(pil_image)
except Exception:
return None, "Please upload a valid image."
def switch_model(selected_model):
gr.Info("Loading model... Please wait.")
return "Loading model... Please wait.", None, None, None
def load_new_model(selected_model):
global model, tokenizer, image_processor
model, tokenizer, image_processor = load_model_and_components(selected_model)
return "Model loaded successfully.", None, None, None
model_selector.change(fn=switch_model, inputs=model_selector, outputs=[loading_message, upload_button, image_display, output_text])
model_selector.change(fn=load_new_model, inputs=model_selector, outputs=[loading_message, upload_button, image_display, output_text])
upload_button.change(fn=handle_uploaded_image, inputs=upload_button, outputs=[image_display, output_text])
predefined_images_display.select(fn=handle_predefined_image, inputs=predefined_images_display, outputs=[image_display, output_text])
interface.launch()
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