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import gradio as gr | |
import os | |
import skimage | |
import matplotlib.pyplot as plt | |
from PIL import Image | |
import numpy as np | |
from collections import OrderedDict | |
import torch | |
from imagebind import data | |
from imagebind.models import imagebind_model | |
from imagebind.models.imagebind_model import ModalityType | |
import torch.nn as nn | |
device = "cpu" #"cuda:0" if torch.cuda.is_available() else "cpu" | |
model = imagebind_model.imagebind_huge(pretrained=True) | |
model.eval() | |
model.to(device) | |
def image_text_zeroshot(text): | |
# labels = [text] | |
# inputs = { | |
# ModalityType.TEXT: data.load_and_transform_text(labels, device) | |
# } | |
# with torch.no_grad(): | |
# embeddings = model(inputs) | |
# # scores = ( | |
# # torch.softmax( | |
# # embeddings[ModalityType.VISION] @ embeddings[ModalityType.TEXT].T, dim=-1 | |
# # ) | |
# # .squeeze(0) | |
# # .tolist() | |
# # ) | |
score_dict = "./assets/ICA-Logo.png" #{label: score for label, score in zip(labels, scores)} | |
return score_dict | |
def main(): | |
iface = gr.Interface( | |
fn=image_text_zeroshot, | |
inputs="text", | |
outputs="file", | |
live=True, | |
capture_session=True, | |
title="Texto para Imagem", | |
description="Digite um texto e obtenha uma imagem com o texto.", | |
allow_flagging=False, | |
) | |
iface.launch() | |
# def image_classifier(inp): | |
# return {'cat': 0.3, 'dog': 0.7} | |
# demo = gr.Interface(fn=image_classifier, inputs="image", outputs="label") | |
# demo.launch() | |