Create app.py

app.py

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+import sys
+if 'google.colab' in sys.modules:
+    print('Running in Colab.')
+    !pip3 install transformers==4.15.0 timm==0.4.12 fairscale==0.4.4
+    !git clone https://github.com/salesforce/BLIP
+    %cd BLIP
+import gradio as gr
+import torch
+import requests
+from torchvision import transforms
+from PIL import Image
+import requests
+import torch
+from torchvision import transforms
+from torchvision.transforms.functional import InterpolationMode
+
+
+#@title
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+model = torch.hub.load('pytorch/vision:v0.6.0', 'resnet18', pretrained=True).eval()
+response = requests.get("https://git.io/JJkYN")
+labels = response.text.split("\n")
+
+def predict(inp):
+  inp = transforms.ToTensor()(inp).unsqueeze(0)
+  with torch.no_grad():
+    prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)
+    confidences = {labels[i]: float(prediction[i]) for i in range(1000)}    
+  return confidences
+
+demo = gr.Interface(fn=predict, 
+             inputs=gr.inputs.Image(type="pil"),
+             outputs=gr.outputs.Label(num_top_classes=3)
+             )
+             
+def load_demo_image(image_size,device,imageurl):
+    img_url = imageurl
+    raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')   
+
+    w,h = raw_image.size
+    display(raw_image.resize((w//5,h//5)))
+    
+    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))
+        ]) 
+    image = transform(raw_image).unsqueeze(0).to(device)   
+    return image
+from models.blip import blip_decoder
+
+def predict(imageurl):
+  image_size = 384
+  image = load_demo_image(image_size=image_size, device=device,imageurl=imageurl)
+
+  model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth'
+      
+  model = blip_decoder(pretrained=model_url, image_size=image_size, vit='base')
+  model.eval()
+  model = model.to(device)
+
+  with torch.no_grad():
+      # beam search
+      caption = model.generate(image, sample=False, num_beams=3, max_length=20, min_length=5) 
+      # nucleus sampling
+      # caption = model.generate(image, sample=True, top_p=0.9, max_length=20, min_length=5) 
+  return('caption: '+caption[0])
+demo = gr.Interface(fn=predict, 
+         inputs="text",
+         outputs=gr.outputs.Label(num_top_classes=3)
+         )   
+demo.launch()