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| import gradio as gr | |
| #import requests | |
| from PIL import Image | |
| from transformers import BlipProcessor, BlipForConditionalGeneration | |
| processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base") | |
| model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base") | |
| #num_captions = gr.Dropdown([1, 2, 3, 4,5],label = "select no.of captions to generate") | |
| def caption_generator(image, num_captions): | |
| num_captions = int(float(num_captions)) | |
| raw_image = Image.fromarray(image).convert('RGB') | |
| inputs = processor(raw_image, return_tensors="pt") | |
| out = model.generate( | |
| **inputs, | |
| num_return_sequences=num_captions, # generate 3 captions | |
| max_length=32, # maximum length of generated captions | |
| early_stopping=True, # stop generating captions when all beam hypotheses have finished | |
| num_beams=num_captions, # number of beams for beam search | |
| no_repeat_ngram_size=2, # avoid repeating n-grams of size 2 or larger | |
| length_penalty=0.8 # higher penalty value will encourage shorter captions | |
| ) | |
| captions = "" | |
| for i, caption in enumerate(out): | |
| captions = captions +processor.decode(caption, skip_special_tokens=True) + " ," | |
| return captions | |
| gr.Interface(caption_generator, inputs= [gr.Image(), gr.Dropdown([1, 2, 3, 4,5],value = [2], label = "select no.of captions to generate")], outputs = gr.outputs.Textbox(), live = True).launch() |