Create app.py

app.py

@@ -0,0 +1,54 @@
+import os
+os.system("git clone https://github.com/salesforce/BLIP")
+os.chdir("BLIP")
+os.system("wget https://upload.wikimedia.org/wikipedia/commons/thumb/e/ec/Mona_Lisa%2C_by_Leonardo_da_Vinci%2C_from_C2RMF_retouched.jpg/1024px-Mona_Lisa%2C_by_Leonardo_da_Vinci%2C_from_C2RMF_retouched.jpg -O mona.jpg")
+
+from PIL import Image
+import requests
+import torch
+from torchvision import transforms
+from torchvision.transforms.functional import InterpolationMode
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+
+
+
+
+import gradio as gr
+
+from models.blip import blip_decoder
+
+image_size = 384
+transform = transforms.Compose([
+    transforms.Resize((image_size,image_size),interpolation=InterpolationMode.BICUBIC),
+    transforms.ToTensor(),
+    transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
+    ]) 
+
+model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model*_base_caption.pth'
+    
+model = blip_decoder(pretrained=model_url, image_size=384, vit='base')
+model.eval()
+model = model.to(device)
+
+    
+def inference(raw_image):
+    image = transform(raw_image).unsqueeze(0).to(device)     
+    with torch.no_grad():
+      caption = model.generate(image, sample=False, num_beams=3, max_length=20, min_length=5)
+      print('caption: '+caption[0])
+
+    return 'caption: '+caption[0]
+    
+inputs = gr.inputs.Image(type='pil')
+outputs = gr.outputs.Textbox(label="Output")
+
+title = "Omnivore"
+
+description = "Gradio demo for Omnivore: A Single Model for Many Visual Modalities. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."
+
+article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2201.08377' target='_blank'>Omnivore: A Single Model for Many Visual Modalities</a> | <a href='https://github.com/facebookresearch/omnivore' target='_blank'>Github Repo</a></p>"
+
+
+gr.Interface(inference, inputs, outputs, title=title, description=description, article=article, examples=[['mona.jpg']]).launch(enable_queue=True,cache_examples=True)