app.py

import requests 
from PIL import Image
from transformers import AutoTokenizer, AutoImageProcessor, VisionEncoderDecoderModel
import gradio as gr
import os
from concurrent.futures import ThreadPoolExecutor

# Load the model, tokenizer, and image processor with error handling
def load_model_and_components(model_name):
    model = VisionEncoderDecoderModel.from_pretrained(model_name)
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    image_processor = AutoImageProcessor.from_pretrained(model_name)
    return model, tokenizer, image_processor

# Preload both models in parallel
def preload_models():
    models = {}
    model_names = ["laicsiifes/swin-distilbertimbau", "laicsiifes/swin-gportuguese-2"]
    with ThreadPoolExecutor() as executor:
        results = executor.map(load_model_and_components, model_names)
    for name, result in zip(model_names, results):
        models[name] = result
    return models

models = preload_models()

# Predefined images for selection
image_folder = "images"
predefined_images = [
    Image.open(os.path.join(image_folder, fname)).convert("RGB")
    for fname in os.listdir(image_folder) \
    if  fname.lower().endswith(('.png', '.jpg', '.jpeg', '.gif', '.bmp', '.ppm')) 
]

# Function to preprocess the image to RGB format
def preprocess_image(image):
    if image is None:
        return None, None
    pil_image = image.convert("RGB")
    return pil_image, None

# Function to process the image and generate a caption
def generate_caption(image, selected_model):
    if image is None:
        return "Please upload an image to generate a caption."
    model, tokenizer, image_processor = models[selected_model]
    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

# Define UI
with gr.Blocks(theme=gr.themes.Citrus(primary_hue="blue", secondary_hue="orange")) as interface:
    gr.Markdown("""
        # Welcome to the LAICSI-IFES space for Vision Encoder-Decoder (VED) demonstration
        ---
        ### Select an available model: Swin-DistilBERTimbau (168M) or Swin-GPorTuguese-2 (240M)
    """)
    with gr.Row(variant='panel'):
        with gr.Column():
            model_selector = gr.Dropdown(
                choices=list(models.keys()),
                value="laicsiifes/swin-distilbertimbau",
                label="Select Model"
            )
            
    gr.Markdown("""
        ---
        ### Upload image or example images below, and click `Generate`
        """)
    
    with gr.Row(variant='panel'):
        with gr.Column():
            image_display = gr.Image(type="pil", label="Image Preview", image_mode="RGB", height=400)
        with gr.Column():
            output_text = gr.Textbox(label="Generated Caption")
            generate_button = gr.Button("Generate")
            
    gr.Markdown("""---""")
    
    with gr.Row(variant='panel'):
            examples = gr.Examples(
                examples=predefined_images,
                fn=preprocess_image,
                inputs=[image_display],
                outputs=[image_display, output_text],
                label="Examples"
            )

    # Define actions
    model_selector.change(fn=lambda: (None, None), outputs=[image_display, output_text])

    image_display.upload(fn=preprocess_image, inputs=[image_display], outputs=[image_display, output_text])
    image_display.clear(fn=lambda: None, outputs=[output_text])

    generate_button.click(fn=generate_caption, inputs=[image_display, model_selector], outputs=output_text)

    interface.launch(share=False)