app.py

import gradio as gr
from transformers import pipeline
import torch

CAPTION_MODELS = {
    'blip-base': 'Salesforce/blip-image-captioning-base',
    'blip-large': 'Salesforce/blip-image-captioning-large',
    'vit-gpt2-coco-en': 'ydshieh/vit-gpt2-coco-en',
    'blip2-2.7b_8bit': 'Mediocreatmybest/blip2-opt-2.7b_8bit',
    'blip2-2.7b-fp16': 'Mediocreatmybest/blip2-opt-2.7b-fp16-sharded',
    'blip2-2.7b': 'Salesforce/blip2-opt-2.7b',
}

# Create a dictionary to store loaded models
loaded_models = {}

# Modify caption_image to accept and process lists of images
def caption_image(model_choice, images_input, urls_input, load_in_8bit, device):
    input_data = images_input if all(i is not None for i in images_input) else urls_input

    model_key = (model_choice, load_in_8bit, device)  # Update the model key to include the device

    # Check if the model is already loaded
    if model_key in loaded_models:
        captioner = loaded_models[model_key]
    else:
        model_kwargs = {"load_in_8bit": load_in_8bit} if load_in_8bit else {}
        dtype = torch.float16 if load_in_8bit else torch.float32  # Set dtype based on the value of load_in_8bit
        captioner = pipeline(task="image-to-text",
                            model=CAPTION_MODELS[model_choice],
                            max_new_tokens=30,
                            device=device, # Set the device as selected by the user
                            model_kwargs=model_kwargs, 
                            torch_dtype=dtype,  # Set the floating point
                            use_fast=True
                            )
        # Store the loaded model
        loaded_models[model_key] = captioner

    captions = captioner(input_data)  # Run the model on the batch of images
    results = [str(caption['generated_text']).strip() for caption in captions]  # Extract the captions from the outputs

    return results

model_dropdown = gr.Dropdown(choices=list(CAPTION_MODELS.keys()), label='Select Caption Model')
image_input = gr.Image(type="pil", label="Input Image")  # Now takes multiple images
url_input = gr.Text(label="Input URL")  # Now takes multiple URLs
load_in_8bit = gr.Checkbox(label="Load model in 8bit")
device = gr.Radio(choices=['cpu', 'cuda'], label='Device')  # Radio button for device selection

iface = gr.Interface(caption_image, inputs=[model_dropdown, image_input, url_input, load_in_8bit, device], outputs=gr.outputs.Textbox(type="auto", label="Caption"))
iface.launch()