Update app.py

app.py

@@ -10,23 +10,24 @@ model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-2.7b
 device = "cuda" if torch.cuda.is_available() else "cpu"
 model.to(device)
 
-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
+# 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)
-             )
+# demo = gr.Interface(fn=predict, 
+#              inputs=gr.inputs.Image(type="pil"),
+#              outputs=gr.outputs.Label(num_top_classes=3)
+#              )
              
 def predict(imageurl):
+    image = Image.open(requests.get(imageurl, stream=True).raw).convert('RGB')
     inputs = processor(image, return_tensors="pt")
     generated_ids = model.generate(**inputs, max_new_tokens=20)
     generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
-  return('caption: '+generated_text)
+    return('caption: '+generated_text)
     
 demo = gr.Interface(fn=predict, 
      inputs="text",