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from transformers import BlipForConditionalGeneration
from transformers import AutoProcessor
from PIL import Image
import requests
import gradio as gr
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
image = processor(image, return_tensors="pt")
generated_ids = model.generate(**image)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
print(generated_text)
def launch(input):
url = input
image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
image = processor(image, return_tensors="pt")
generated_ids = model.generate(**image)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
return generated_text
iface = gr.Interface(fn=launch, inputs="text", outputs="text")
iface.launch()
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