float output

app.py

@@ -15,16 +15,16 @@ def predict(image, labels):
         logits_per_image, logits_per_text = model(image, text)
         probs = logits_per_image.softmax(dim=-1).cpu().numpy()
 
-    return {k: v for k, v in zip(labels, probs[0])}
+    return {k: float(v) for k, v in zip(labels, probs[0])}
 
-# probs = predict(Image.open("../CLIP/CLIP.png"), ["cat", "dog", "ball"])
+# probs = predict(Image.open("../CLIP/CLIP.png"), "cat, dog, ball")
 # print(probs)
 
 
 gr.Interface(fn=predict,
              inputs=[
                  gr.inputs.Image(label="Image to classify.", optional=True, type="pil"),
-                 gr.inputs.Textbox(lines=1, label="Comma separated classes", placeholder="Enter your classes separated by ','..",)],
+                 gr.inputs.Textbox(lines=1, label="Comma separated classes", placeholder="Enter your classes separated by ','",)],
              theme="grass",
              outputs="label",
              description="Zero Shot Image classification..").launch()