Update app.py

app.py

@@ -1,59 +1,58 @@
-from transformers import pipeline
-import gradio as gr
-
-
-clip_models = [
-  "zer0int/CLIP-GmP-ViT-L-14",
-  "John6666/zer0int_CLIP-GmP-ViT-L-14",
-  "openai/clip-vit-large-patch14",
-  "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k",
-]
-
-clip_checkpoint = clip_models[0]
-clip_detector = pipeline(model=clip_checkpoint, task="zero-shot-image-classification")
-
-def postprocess(output):
-  return {out["label"]: float(out["score"]) for out in output}
-
-
-def infer(image, candidate_labels):
-  candidate_labels = [label.lstrip(" ") for label in candidate_labels.split(",")]
-  clip_out = clip_detector(image, candidate_labels=candidate_labels)
-  return postprocess(clip_out)
-
-
-def load_clip_model(modelname):
-  global clip_detector
-  try:
-    clip_detector = pipeline(model=modelname, task="zero-shot-image-classification")
-  except Exception as e:
-    raise gr.Error(f"Model load error: {modelname} {e}")
-  return modelname
-
-
-with gr.Blocks() as demo:
-  gr.Markdown("# Test CLIP")
-  with gr.Row():
-    with gr.Column():
-        image_input = gr.Image(type="pil")
-        text_input = gr.Textbox(label="Input a list of labels")
-        model_input = gr.Dropdown(label="CLIP model", choices=clip_models, value=clip_models[0], allow_custom_value=True, interactive=True)
-        run_button = gr.Button("Run", visible=True)
-
-    with gr.Column():
-      clip_output = gr.Label(label = "CLIP Output", num_top_classes=3)
-      
-  examples = [["./baklava.jpg", "baklava, souffle, tiramisu"], ["./cheetah.jpg", "cat, dog"], ["./cat.png", "cat, dog"]]
-  gr.Examples(
-        examples = examples, 
-        inputs=[image_input, text_input],
-        outputs=[clip_output],
-        fn=infer,
-        cache_examples=True
-    )
-  run_button.click(fn=infer,
-                    inputs=[image_input, text_input],
-                    outputs=[clip_output])
-  model_input.change(load_clip_model, [model_input], [model_input])
-
+from transformers import pipeline
+import gradio as gr
+
+
+clip_models = [
+  "zer0int/CLIP-GmP-ViT-L-14",
+  "openai/clip-vit-large-patch14",
+  "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k",
+]
+
+clip_checkpoint = clip_models[0]
+clip_detector = pipeline(model=clip_checkpoint, task="zero-shot-image-classification")
+
+def postprocess(output):
+  return {out["label"]: float(out["score"]) for out in output}
+
+
+def infer(image, candidate_labels):
+  candidate_labels = [label.lstrip(" ") for label in candidate_labels.split(",")]
+  clip_out = clip_detector(image, candidate_labels=candidate_labels)
+  return postprocess(clip_out)
+
+
+def load_clip_model(modelname):
+  global clip_detector
+  try:
+    clip_detector = pipeline(model=modelname, task="zero-shot-image-classification")
+  except Exception as e:
+    raise gr.Error(f"Model load error: {modelname} {e}")
+  return modelname
+
+
+with gr.Blocks() as demo:
+  gr.Markdown("# Test CLIP")
+  with gr.Row():
+    with gr.Column():
+        image_input = gr.Image(type="pil")
+        text_input = gr.Textbox(label="Input a list of labels")
+        model_input = gr.Dropdown(label="CLIP model", choices=clip_models, value=clip_models[0], allow_custom_value=True, interactive=True)
+        run_button = gr.Button("Run", visible=True)
+
+    with gr.Column():
+      clip_output = gr.Label(label = "CLIP Output", num_top_classes=3)
+      
+  examples = [["./baklava.jpg", "baklava, souffle, tiramisu"], ["./cheetah.jpg", "cat, dog"], ["./cat.png", "cat, dog"]]
+  gr.Examples(
+        examples = examples, 
+        inputs=[image_input, text_input],
+        outputs=[clip_output],
+        fn=infer,
+        cache_examples=True
+    )
+  run_button.click(fn=infer,
+                    inputs=[image_input, text_input],
+                    outputs=[clip_output])
+  model_input.change(load_clip_model, [model_input], [model_input])
+
 demo.launch()