Spaces:
Sleeping
Sleeping
from transformers import BlipProcessor, BlipForQuestionAnswering | |
from PIL import Image | |
import gradio as gr | |
processor = BlipProcessor.from_pretrained("Salesforce/blip-vqa-base") | |
model = BlipForQuestionAnswering.from_pretrained("Salesforce/blip-vqa-base") | |
def predict(image, question): | |
inputs = processor(text=question, images=image, return_tensors="pt") | |
out = model.generate(**inputs, max_new_tokens=100, num_beams=10, temperature=0.7) | |
answer = processor.decode(out[0], skip_special_tokens=True) | |
return answer | |
iface = gr.Interface( | |
fn=predict, | |
inputs=[ | |
gr.Image(type="pil"), | |
gr.Textbox(lines=2, placeholder="Faça sua pergunta sobre a imagem"), | |
], | |
outputs="text", | |
title="Resposta Visual a Perguntas com BLIP", | |
description="Faça upload de uma imagem e faça uma pergunta sobre ela. O modelo BLIP tentará responder!", | |
) | |
iface.launch(share=True) |