| import gradio as gr | |
| from PIL import Image | |
| from transformers import BlipProcessor, BlipForConditionalGeneration | |
| import re | |
| import string | |
| import nltk | |
| nltk.download('stopwords') | |
| nltk.download('wordnet') | |
| processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base") | |
| model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base") | |
| def hashtag_generator(image): | |
| raw_image = Image.fromarray(image).convert('RGB') | |
| inputs = processor(raw_image, return_tensors="pt") | |
| out = model.generate( | |
| **inputs, | |
| num_return_sequences=4, | |
| max_length=32, | |
| early_stopping=True, | |
| num_beams=4, | |
| no_repeat_ngram_size=2, | |
| length_penalty=0.8 | |
| ) | |
| captions = "" | |
| for i, caption in enumerate(out): | |
| captions = captions +processor.decode(caption, skip_special_tokens=True) + " ," | |
| text = "".join([word.lower() for word in captions if word not in string.punctuation]) | |
| tokens = re.split('\W+', text) | |
| text = [wn.lemmatize(word) for word in tokens if word not in stopwords] | |
| words = set(text) | |
| hashtags = "" | |
| for hashtag in words: | |
| if len(hashtag) == 0: | |
| pass | |
| else: | |
| hashtags = hashtags + f" ,#{hashtag}" | |
| return hashtags[2:] | |
| gr.Interface(hashtag_generator, inputs= gr.inputs.Image(), outputs = gr.outputs.Textbox(), live = True).launch() | |