label and user text

demo.py

@@ -6,14 +6,16 @@ import gradio as gr
 
 # Load the model
 device = "cuda" if torch.cuda.is_available() else "cpu"
-model, transform = clip.load("ViT-B/32", device=device)
+model, preprocess = clip.load('ViT-B/32', device)
 
 # Download the dataset
 cifar100 = CIFAR100(root=os.path.expanduser("~/.cache"), download=True, train=False)
 
-def classify(img):
-    image = transform(img).unsqueeze(0).to(device)
-    text_inputs = torch.cat([clip.tokenize(f"a photo of a {c}") for c in cifar100.classes]).to(device)
+def classify(img, user_text):
+    image = preprocess(img).unsqueeze(0).to(device)
+    user_texts = user_text.split(",")
+    text_sources = cifar100.classes + user_texts
+    text_inputs = torch.cat([clip.tokenize(f"a photo of a {c}") for c in text_sources]).to(device)
 
     # Calculate features
     with torch.no_grad():
@@ -26,14 +28,16 @@ def classify(img):
     similarity = (100.0 * image_features @ text_features.T).softmax(dim=-1)
     values, indices = similarity[0].topk(5)
 
-    text=""
-    # Print the result
+    result = {}
     for value, index in zip(values, indices):
-        text+=f"{cifar100.classes[index]:>16s}: {100 * value.item():.2f}%\n"
-    return text
+        result[text_sources[index]] = value.item()
+    return result
 
-inputs = gr.inputs.Image(type='pil', label="Original Image")
-outputs = gr.outputs.Textbox(type="str", label="Text Output")
+inputs = [
+  gr.inputs.Image(type='pil', label="Original Image"),
+  gr.inputs.Textbox(lines=1)
+]
+outputs = gr.outputs.Label(type="confidences",num_top_classes=5)
 
 title = "CLIP"
 description = "CLIP demo"