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import gradio as gr
from transformers import AutoProcessor, AutoModelForCausalLM, MarianMTModel, MarianTokenizer
from PIL import Image
import torch
from gtts import gTTS
import os
# Funções auxiliares
def prepare_image(image_path):
image = Image.open(image_path).convert("RGB")
inputs = processor(images=image, return_tensors="pt").to(device)
return image, inputs.pixel_values
def generate_caption(pixel_values):
model.eval()
with torch.no_grad():
generated_ids = model.generate(
pixel_values=pixel_values,
max_length=50,
num_beams=4,
early_stopping=True,
no_repeat_ngram_size=2
)
return processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
def translate_to_portuguese(text):
inputs = translation_tokenizer(text, return_tensors="pt", truncation=True).to(device)
translated_ids = translation_model.generate(inputs["input_ids"], max_length=50, num_beams=4, early_stopping=True)
return translation_tokenizer.batch_decode(translated_ids, skip_special_tokens=True)[0]
def text_to_speech_gtts(text, lang='pt'):
tts = gTTS(text=text, lang=lang)
tts.save("output.mp3")
return "output.mp3"
# Carregar os modelos
processor = AutoProcessor.from_pretrained("microsoft/git-base")
model = AutoModelForCausalLM.from_pretrained("microsoft/git-base")
translation_model_name = 'Helsinki-NLP/opus-mt-tc-big-en-pt'
translation_tokenizer = MarianTokenizer.from_pretrained(translation_model_name)
translation_model = MarianMTModel.from_pretrained(translation_model_name)
# Configurar o dispositivo (GPU ou CPU)
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)
translation_model.to(device)
# Função principal para processar a imagem e gerar a voz
def process_image(image):
_, pixel_values = prepare_image(image)
caption_en = generate_caption(pixel_values)
caption_pt = translate_to_portuguese(caption_en)
audio_file = text_to_speech_gtts(caption_pt)
return caption_pt, audio_file
# Caminhos para as imagens de exemplo (supondo que estejam no mesmo diretório que o script)
example_image_paths = [
"example1.png",
"example2.png",
"example3.png"
]
# Interface Gradio
iface = gr.Interface(
fn=process_image,
inputs=gr.Image(type="filepath"),
outputs=[gr.Textbox(), gr.Audio(type="filepath")],
examples=example_image_paths,
title="Image to Voice",
description="Gera uma descrição em português e a converte em voz a partir de uma imagem."
)
if __name__ == "__main__":
iface.launch()
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