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import io
import requests
import gradio as gr
# from transformers import AutoModel, AutoTokenizer
from huggingface_hub import list_models
from datasets import load_dataset
from typing import List
from PIL import Image
def get_image_names(dataset):
return [str(i) for i in range(len(dataset))]
def get_image_from_dataset(index):
image_data = dataset[int(index)]["image"]
return image_data
def process_image(image=None, dataset_image_index=None):
if dataset_image_index:
image = get_image_from_dataset(dataset_image_index)
return image
def create_interface(tag, image_indices):
""" Create Gradio interface"""
iface = gr.Interface(
fn=process_image,
inputs=[
gr.Dropdown(choices=get_collection_models(tag), label="Select Model"),
gr.Image(type="pil", label="Upload Image"),
gr.Dropdown(choices=image_indices, label="Select one from MERIT Dataset test-set"),
],
outputs=gr.Image(label="Output Image"),
title="Saliency Visualization",
description="Upload your image or select one from the MERIT Dataset test-set."
)
return iface
def get_collection_models(tag: str) -> List[str]:
"""Get a list of models from a specific Hugging Face collection."""
models = list_models(author="de-Rodrigo")
model_names = []
for model in models:
if tag in model.tags:
model_names.append(model.modelId)
return model_names
def load_model(model_name: str):
"""Load a model from Hugging Face Hub."""
model = AutoModel.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
return model, tokenizer
# # Example processing function
# def process_input(text: str, model_name: str) -> str:
# model, tokenizer = load_model(model_name)
# inputs = tokenizer(text, return_tensors="pt")
# outputs = model(**inputs)
# return f"Processed output with {model_name}"
dataset_name = "de-Rodrigo/merit"
dataset = load_dataset(dataset_name, name="en-digital-seq", split="train", num_proc=8)
image_indices = get_image_names(dataset)
models_tag = "saliency-merit"
iface = create_interface(models_tag, image_indices)
iface.launch()
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