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| import numpy as np | |
| import requests | |
| import gradio as gr | |
| from transformers import pipeline | |
| translator = pipeline("translation", model="Helsinki-NLP/opus-mt-en-de") | |
| def blipofasinki(input_img): | |
| b64_string = gr.processing_utils.encode_url_or_file_to_base64(input_img) | |
| #blip-Nucleus | |
| responsen = requests.post(url='https://hf.space/embed/Salesforce/BLIP/+/api/predict/', json={"data": [ b64_string,"Image Captioning","None",str('Nucleus sampling')]}) | |
| jresn = responsen.json() | |
| capn = jresn["data"][0] | |
| offset = len(str("caption:")) | |
| capn = capn[offset:] | |
| trans_capn = translator(capn) | |
| tcn = trans_capn[0]['translation_text'] | |
| #blip-beam | |
| responseb = requests.post(url='https://hf.space/embed/Salesforce/BLIP/+/api/predict/', json={"data": [ b64_string,"Image Captioning","None",str('Beam search')]}) | |
| jresb = responseb.json() | |
| capb = jresb["data"][0] | |
| capb = capb[offset:] | |
| trans_capb = translator(capb) | |
| tcb = trans_capb[0]['translation_text'] | |
| #ofa | |
| responseo = requests.post(url='https://hf.space/embed/OFA-Sys/OFA-Image_Caption/+/api/predict/', json={"data": [b64_string]}) | |
| jreso = responseo.json() | |
| capo = jreso["data"][0] | |
| trans_capo = translator(capo) | |
| tco = trans_capo[0]['translation_text'] | |
| return [tcn, tcb, tco] | |
| description = "A direct comparison in image captioning between BLIP and OFA (in German translated with Helsinki)." | |
| input_ = [gr.inputs.Image(type='filepath', label="Input Image")] | |
| output_ = [gr.outputs.Textbox(label="BLIP Nucleus sampling output"),gr.outputs.Textbox(label="BLIP Beam search output"), gr.outputs.Textbox(label="OFA output")] | |
| iface = gr.Interface(blipofasinki, input_, output_, description=description) | |
| iface.launch(debug=True,show_error=True) |