Update app.py

app.py

@@ -1,21 +1,82 @@
 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
 from transformers import BlipProcessor, BlipForConditionalGeneration
 
 model_id = "Salesforce/blip-image-captioning-base"
 
 model = BlipForConditionalGeneration.from_pretrained(model_id)
-processor = BlipProcessor.from_pretrained(model_id)
 
-##
 
-def launch(input):
-    image = Image.open(requests.get(input, stream=True).raw).convert('RGB')
-    inputs = processor(image, return_tensors="pt")
-    out = model.generate(**inputs)
-    return processor.decode(out[0], skip_special_tokens=True)
+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_large_caption.pth'
+    
+# model = blip_decoder(pretrained=model_url, image_size=384, vit='large')
+model.eval()
+model = model.to(device)
+
+
+# from models.blip_vqa import blip_vqa
+
+# image_size_vq = 480
+# transform_vq = transforms.Compose([
+#     transforms.Resize((image_size_vq,image_size_vq),interpolation=InterpolationMode.BICUBIC),
+#     transforms.ToTensor(),
+#     transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
+#     ]) 
+
+# model_url_vq = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model*_vqa.pth'
+    
+# model_vq = blip_vqa(pretrained=model_url_vq, image_size=480, vit='base')
+# model_vq.eval()
+# model_vq = model_vq.to(device)
+
+
+
+def inference(raw_image, model_n, question, strategy):
+    if model_n == 'Image Captioning':
+        image = transform(raw_image).unsqueeze(0).to(device)   
+        with torch.no_grad():
+          if strategy == "Beam search":
+              caption = model.generate(image, sample=False, num_beams=3, max_length=20, min_length=5)
+          else:
+              caption = model.generate(image, sample=True, top_p=0.9, max_length=20, min_length=5)
+          return 'caption: '+caption[0]
+
+    else:   
+        image_vq = transform_vq(raw_image).unsqueeze(0).to(device)  
+        with torch.no_grad():
+            answer = model_vq(image_vq, question, train=False, inference='generate') 
+        return  'answer: '+answer[0]
+    
+# inputs = [gr.inputs.Image(type='pil'),gr.inputs.Radio(choices=['Image Captioning',"Visual Question Answering"], type="value", default="Image Captioning", label="Task"),gr.inputs.Textbox(lines=2, label="Question"),gr.inputs.Radio(choices=['Beam search','Nucleus sampling'], type="value", default="Nucleus sampling", label="Caption Decoding Strategy")]
+inputs = [gr.inputs.Image(type='pil'),gr.inputs.Radio(choices=['Image Captioning'], type="value", default="Image Captioning", label="Task"),gr.inputs.Textbox(lines=2, label="Question"),gr.inputs.Radio(choices=['Beam search','Nucleus sampling'], type="value", default="Nucleus sampling", label="Caption Decoding Strategy")]
+
+outputs = gr.outputs.Textbox(label="Output")
+
+title = "BLIP"
+
+description = "Gradio demo for BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation (Salesforce Research). 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/2201.12086' target='_blank'>BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation</a> | <a href='https://github.com/salesforce/BLIP' target='_blank'>Github Repo</a></p>"
+
 
-iface = gr.Interface(launch, inputs="text", outputs="text")
-iface.launch()
+gr.Interface(inference, inputs, outputs, title=title, description=description, article=article, examples=[['starrynight.jpeg',"Image Captioning","None","Nucleus sampling"]]).launch(enable_queue=True)